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A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts



The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details during the analytical procedures and on quality issues, this research sheds light on the efficacy of using BERTopic and NMF to analyze Twitter data.
published: 06 May 2022
doi: 10.3389/fsoc.2022.886498
Frontiers in Sociology | 1May 2022 | Volume 7 | Article 886498
Edited by:
Dimitri Prandner,
Johannes Kepler University of
Linz, Austria
Reviewed by:
Tobias Wolbring,
University of Erlangen
Nuremberg, Germany
Ruben Bach,
University of Mannheim, Germany
Joanne Yu
Specialty section:
This article was submitted to
Sociological Theory,
a section of the journal
Frontiers in Sociology
Received: 28 February 2022
Accepted: 19 April 2022
Published: 06 May 2022
Egger R and Yu J (2022) A Topic
Modeling Comparison Between LDA,
NMF, Top2Vec, and BERTopic to
Demystify Twitter Posts.
Front. Sociol. 7:886498.
doi: 10.3389/fsoc.2022.886498
A Topic Modeling Comparison
Between LDA, NMF, Top2Vec, and
BERTopic to Demystify Twitter Posts
Roman Egger 1and Joanne Yu2
1Innovation and Management in Tourism, Salzburg University of Applied Sciences, Salzburg, Austria, 2Department of
Tourism and Service Management, Modul University Vienna, Vienna, Austria
The richness of social media data has opened a new avenue for social science research to
gain insights into human behaviors and experiences. In particular, emerging data-driven
approaches relying on topic models provide entirely new perspectives on interpreting
social phenomena. However, the short, text-heavy, and unstructured nature of social
media content often leads to methodological challenges in both data collection and
analysis. In order to bridge the developing field of computational science and empirical
social research, this study aims to evaluate the performance of four topic modeling
techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization
(NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and
digital media, this research takes Twitter posts as the reference point and assesses
the performance of different algorithms concerning their strengths and weaknesses in
a social science context. Based on certain details during the analytical procedures and
on quality issues, this research sheds light on the efficacy of using BERTopic and NMF
to analyze Twitter data.
Keywords: topic model, machine learning, LDA, Top2Vec, BERTopic, NMF, Twitter, covid travel
With its limitless availability of constantly growing datasets and simultaneous increase in
computing power, the era of digital transformation has brought about the potential to alter social
science (Lazer and Radford, 2017). These massive volumes of data assemble digital footprints
and capture cumulative human activities, both individually and collectively (Boccia Artieri et al.,
2021). As such, the rise of big data in the twenty-first century has prompted a demand for
advanced analytic techniques such as machine learning, natural language processing (NLP), and
topic modeling in order to uncover patterns and relations embedded in the data, reduce the
dimensionality of data, and forecast future outcomes more effectively (Elragal and Klischewski,
2017). In particular, the use of topic modeling in social science [e.g., conventional models such as
Dirichlet allocation (LDA) and non-negative matrix factorization (NMF)] has soared in popularity
across various domains in the past years (Maier et al., 2018; Chen et al., 2019). These techniques rely
on statistical modeling to extract topical patterns within a collection of texts (Egger and Yu, 2021).
For instance, since a semantic relationship exists between terms like “apple,” “pear,” and “mango,”
they could be formed under a topic called “fruit” in a text corpus (i.e., a collection of documents).
Typically, documents contain mixed membership, which means that a mixture of topics exists in
the corpus (Maier et al., 2018).
Egger and Yu A Topic Modeling Comparison
To unfold the complex nature of social phenomena, topic
models act as a bridge between social science and (un)structured
analysis, different methods of reasoning, and big data analytics
(Hannigan et al., 2019) due to their explorative character
(Albalawi et al., 2020). In social science, implications of big
data can range from macro-level analyses (e.g., social structure
and human behavior) to micro-level analyses (e.g., individual
relationships and aspects of daily activities). Based on observed
phenomena and experiences, examples can be noted from a
growing amount of literature analyzing the news (Chen et al.,
2019), online reviews (Bi et al., 2019), and social media content
(Yu and Egger, 2021), amongst others. Yet, while the discussion
of big data in social science mainly circles around the critical
perspective of the subject, the application itself is hardly ever
deliberated. Although big data seems exceptionally promising,
data is always preconfigured through beliefs and values, and
numerous challenges must be acknowledged as every step in
big data analysis depends on various decisive criteria, such as
the selection of parameters, the evaluation of partial results, and
the actual interpretations thereof (Lupton, 2015). With recent
advancement in the NLP field, emerging modeling techniques
such as BERTopic (Grootendorst, 2022) and Top2Vec (Angelov,
2020) further complicate the process of big data analytics,
pressing the need to evaluate the performance of different
algorithms. Additionally, while social scientists are interested in
theory-based assumptions and their implications, data scientists
focus on discovering new patterns (Cai and Zhou, 2016) that
appear to be irrational due to their limited explanatory power for
social phenomena (McFarland et al., 2016).
Social media has opened an entirely new path for social
science research, especially when it comes to the overlap between
human relations and technology. In this respect, the value of
user-generated content on social media platforms has been well-
established and acknowledged since their rich and subjective
information allows for favorable computational analysis (Hu,
2012). For instance, recent research explored the social dynamics
of sporting events based on Facebook comments (Moreau et al.,
2021), while another study disclosed the social semiotics of
different attractions using Instagram content (Arefieva et al.,
2021). Scholars have also used Twitter posts related to the
COVID-19 pandemic to construct individual’s reactions (Boccia
Artieri et al., 2021). From an epistemological viewpoint, what
is common among these data-driven approaches is that they
provide brand-new perspectives on interpreting a phenomenon
and have the possibility to revamp state-of-the-art knowledge
(Simsek et al., 2019). After all, many aspects of social science and
social media intertwine in one way or another; while the former
concerns human interaction, the latter escalates its essence to a
much broader and global scale.
Nevertheless, despite the prominence of social media in
today’s society, posts are often text-heavy and unstructured,
thereby complicating the process of data analysis (Egger and Yu,
2021). Such methodological challenges are particularly salient for
those lacking programming knowledge and skills (Kraska et al.,
2013). Certainly, recent advancements in visual programming
software have enabled researchers to analyze social media data
in a coding-free manner using topic modeling (Yu and Egger,
2021), yet the validity and quality of the findings based on
such intuition remain questionable. One common misconception
that may skew results is the use of default hyperparameter
settings. Although the importance of model tuning has been
frequently acknowledged (Zhou et al., 2017), little guidance can
be found when analyzing social media data in social science. In
addition, another barrier that hinders knowledge generation in
social science contexts is the application of more traditional and
commonly-adopted algorithms (Blair et al., 2020). For example,
despite the popularity of LDA, the reliability and validity of
results have been criticized since model evaluation is left behind
(Egger and Yu, 2021).
Consequently, some social scientists have initiated a call to
conduct more interdisciplinary research and evaluate model
performance based on other new and emerging techniques
(Reisenbichler and Reutterer, 2019; Albalawi et al., 2020; Egger
and Yu, 2021). Appertaining to the insufficient knowledge of
newly developed algorithms that could better handle the nature
of social media data in social science, this study thus aims to
evaluate and compare the performance of four topic modeling
techniques, namely, LDA, NMF, Top2Vec, and BERTopic.
Specifically, LDA is a generative statistical model, NMF uses a
linear algebra approach for topic extraction, and BERTopic and
Top2Vec use an embedding approach. By bridging the discipline
of data science with social science, reviews of the strengths,
and weaknesses of different tools are valuable to support
applied social scientists in choosing appropriate methods. This
research sheds light on the capabilities of alternative solutions
that can facilitate social science scholars in coping with any
methodological issues when addressing big data.
Making Sense of Social Media Using
Machine Learning Models
With the omnipresent use of technologies, human
communication has transcended time and space, both locally and
globally (Joubert and Costas, 2019). Among the various types of
communication tools, social media stands out as a vital medium
in mediating and facilitating interactions between social actors
(Murthy, 2012). As social media portrays human behavior and
interactions, social scientists have proceeded with data mining
(Boccia Artieri et al., 2021) and using NLP and machine learning
approaches. In order to understand the vast numbers of posts
shared on social media, NLP can comprehend human languages,
as programmed for machines, to make predictions based on
the observed social phenomena (Hannigan et al., 2019). On the
other side, machine learning, as a part of artificial intelligence,
refers to computational methods using existing databases (i.e.,
the training data) to build and train a model for prediction and
better decision making (Zhou et al., 2017). The advantages of
opening new horizons for sociological consideration through
advanced data analytics can be witnessed in manifold contexts,
including business, healthcare, education, and, more generally,
the role of social activities in developing scientific knowledge
(Yang et al., 2020).
Frontiers in Sociology | 2May 2022 | Volume 7 | Article 886498
Egger and Yu A Topic Modeling Comparison
Previous research has underlined that the digital revolution
presents dynamics in exchange networks (Joubert and Costas,
2019) and implies one’s self-perception (Murthy, 2012). Examples
can be seen from microblogging sites such as Twitter,
accumulating over 200 million daily active users. As social media
transforms interactions into relationships, and those interactions
evolve into experiences (Witkemper et al., 2012), continuous
status updates are seen and valued as self-production (Murthy,
2012) and, thus, allow scientists to assess perspectives from the
public’s point of view (Joubert and Costas, 2019). For instance,
in infodemiology, Xue et al. (2020) applied machine learning
models to monitor public responses in relation to the COVID-
19 discussion and concerns on Twitter. Likewise, in the highly-
dynamic tourism industry, Lu and Zheng (2021) were able to
track public opinions toward cruise ships during the COVID-
19 pandemic based on collected tweets. Furthermore, unlike
most networking platforms built upon existing friendships, the
retweet function can disseminate information much faster (Park
et al., 2016), thereby making Twitter an ideal medium for social
science research.
Yet, regardless of which social media platform, theorization
remains an integral part (Müller et al., 2016) of the emerging
subject of big data in social science. Although some scholars
believe that big data can, and should, be free of theory altogether
(Anderson, 2008; Kitchin, 2014), it seems improbable to interpret
results without a sufficient understanding of the social sciences
(Mazanec, 2020). Nevertheless, methodological challenges
often present themselves in parallel with epistemological
developments. For instance, because algorithms are unable to
structure free text, data preprocessing steps that require complex
decision-making skills, such as cleaning, transformation, feature
extraction, and vectorization, lay the foundation for further
analysis (Albalawi et al., 2020). Though social scientists have
the ability to preprocess the datasets, issues may arise in the
following steps involving model evaluation and hyperparameter
tuning (Blair et al., 2020). For the most part, these challenges can
be traced back to the nature of social media content itself, which
primarily consists of short, concise, text-heavy, and unstructured
formats (Albalawi et al., 2020).
Topic Modeling as a Solution to Cope With
Unstructured Text Data
As human language is an adaptive multilevel system, text length,
syntactic complexity, and semantic plausibility have long been
considered focal points in both psychology and linguistics
(Bradley and Meeds, 2002). Together with the interplay between
technology and modernization, their impact has also extended to
social media. For instance, scholars have pointed out that shorter
posts typically lead to a higher engagement rate on Facebook
(Sabate et al., 2014), potentially because concise messages reduce
the amount of cognitive effort needed for information processing
(She et al., 2022). Across the various available types of platforms,
Twitter, in particular, restricts each post to a maximum of
280 characters (Queiroz, 2018), and although these short and
unstructured posts conform with social media practice, they
increase the complexity for algorithms to make sense of digital
interaction. Common challenges arise from using compound
words, acronyms, and ungrammatical sentences (Ariffin and
Tiun, 2020). Despite the productive and unexpressed nature of
compound words they often complicate computational analysis
(Krishna et al., 2016). Other difficulties emerge when data are
meaningless (i.e., noisy data) or when there are many gaps
present in the data (i.e., sparse data; Kasperiuniene et al., 2020).
In order to effectively extract features from a large corpus
of text data, numerous text mining approaches have been
introduced (Li et al., 2019), among which topic modeling
serves as the most frequently adopted technique (Hong and
Davison, 2010). In a nutshell, a topic model is a form of
statistical modeling used in machine learning and NLP, as
discussed earlier, that identifies hidden topical patterns within
a collection of texts (Guo et al., 2017). Those viewed as
the most established, go-to techniques include LDA, latent
semantic analysis (LSA), and probabilistic LSA (Albalawi et al.,
2020). More recently, however, newly developed algorithms
such as NMF, Corex, Top2Vec, and BERTopic have also
received, and are continuing to attract, increasing attention from
researchers (Obadimu et al., 2019; Sánchez-Franco and Rey-
Moreno, 2022). In the social sciences, topic models have formerly
been applied to, for example, discover consumers’ implicit
preferences (Vu et al., 2019; Egger et al., 2022), identify semantic
structures on Instagram (Egger and Yu, 2021), and improve
recommendation systems (Shafqat and Byun, 2020). Despite the
robustness of topic modeling algorithms, existing literature relies
primarily on one single model, with LDA being the dominant
method (Gallagher et al., 2017) and is typically viewed as the
standard approach.
Regardless of the popularity of LDA within the social science
branch, its efficacy in analyzing social media data has been
highly criticized (Egger and Yu, 2021; Sánchez-Franco and
Rey-Moreno, 2022). In the case of Twitter data, Jaradat and
Matskin (2019) argue that, while multiple topics can coexist
in a document, LDA tends to neglect co-occurrence relations.
Likewise, other researchers emphasize that noisy and sparse
datasets are unsuitable for LDA (Chen et al., 2019) due to
a lack of features for statistical learning (Cai et al., 2018).
Consequently, researchers have reinforced the value of newly
developed algorithms as alternatives since they often outperform
LDA, especially when analyzing short text data on social media
(Egger, 2022b). Albeit new approaches have emerged and
have been adopted to reveal novel insights, their innovative
advantages (unintentionally) lower the significance of model
evaluation. Evidence can be taken from social media research,
to which applying evaluation techniques is yet to become
mainstream (Reisenbichler and Reutterer, 2019). Furthermore,
because models would be optimized in extracting any slight
variant of a topic, depending on the purpose of the algorithm,
the results might be skewed in a specific direction. These issues
further highlight the unreliability of concentrating solely on one
single topic model and, thereby, also strengthening the value
and need to compare differing algorithms (Reisenbichler and
Reutterer, 2019; Albalawi et al., 2020; Egger and Yu, 2021).
Frontiers in Sociology | 3May 2022 | Volume 7 | Article 886498
Egger and Yu A Topic Modeling Comparison
Intrigued by the complexity of short-text social media data,
the goal of this research is to compare different types of topic
modeling algorithms in order to offer new insights and solutions
to social scientists interested in investigating human interactions.
Compared to other platforms, Twitter features concise posts,
with a maximum of 280 characters per tweet, that can be
identified via specific hashtags (Queiroz, 2018). The use of
hashtags thus streamlines the information search process based
on users’ interests. Seeing the potential of social media in
enhancing crisis communication (Femenia-Serra et al., 2022),
this study makes use of Twitter posts related to travel and the
COVID-19 pandemic as reference points for the evaluation of the
four above-mentioned topic models (i.e., LDA, NMF, Top2Vec,
and BERTopic). The detailed implementation process of this
study proceeded as below.
Data Collection and Preprocessing
Data collection was conducted in November 2021 by using the
data extraction software tool Phantombuster and searching for
the terms #covidtravel as well as the combination of #covid
and #travel to fetch tweets. The initial datasets included a total
of 50,000 tweets posted in English; however, after cleaning
the data and removing duplicate posts, the final datasets
consisted of 31,800 unique tweets. After that, the data underwent
preprocessing in which all mentions (e.g., @users), hashtags,
unknown signs, and emojis were removed. It is important to note
that, up to this point, original sentences were used for BERTopic
and Top2Vec since both algorithms rely on an embedding
approach, and keeping the original structure of the text is vital
for transformer models.
On the other hand, the data for LDA and NMF was
preprocessed further using NLP modules in Python. More
precisely, stopwords were excluded, irrelevant text (e.g., numbers,
abbreviations, and unknown characters) was removed, and
tokenization was performed. Following this step, stemming and
lemmatization were then conducted. The former process used
Porter Stemmer to remove suffixes from words (e.g., investigating
to investigate), whereas the latter used WordNet Lemmatizer
to remove inflectional endings and to return a word to its
base form (e.g., investigating to investigate). Lastly, the text was
converted into term frequency-inverse document frequency (TF-
IDF) weight for information retrieval based on the importance of
a keyword.
Implementation of Topic Models
Model 1: Latent Dirichlet Allocation
LDA, the most popular topic modeling technique, is a generative
probabilistic model for discrete datasets such as text corpora
(Blair et al., 2020). It is considered a three-level hierarchical
Bayesian model, where each collection item is represented as a
finite mixture over an underlying set of topics, and each topic
is represented as an infinite mixture over a collection of topic
probabilities. Hence, as the number of topics need not be pre-
defined (Maier et al., 2018), applying LDA provides researchers
with an efficient resource to obtain an explicit representation of
a document.
In this research, to pinpoint optimal values for the three
hyperparameters required for LDA, a grid search was performed
for the number of topics (K) as well as for beta and alpha.
The higher the beta, the more words the topics consist of;
likewise, the higher the alpha, the more diverse the topics are.
The search for an optimal number of topicsstarted with a range
from two to 15, with a step of one. In the first step of the
learning process, Kwas pre-defined, and the search for beta
and alpha was applied accordingly. During the process, only
one hyperparameter varied, and the other remained unchanged
until reaching the highest coherence score. The coherence score,
referring to the quality of the extracted topics, presented itself
for 14 topics with a value of 0.52. The grid search then yielded
a symmetric distribution with a value of 0.91 for both alpha and
beta. Finally, to facilitate a clear interpretation of the extracted
information from a fitted LDA topic model, pyLDAvis was
used to generate an intertropical distance map (Islam, 2019). A
screenshot of the statistical proximity of the topics can be seen
in Figure 1. An interactive visualization is available at https://
Model 2: Non-negative Matrix Factorization
In contrast to LDA, NMF is a decompositional, non-probabilistic
algorithm using matrix factorization and belongs to the group of
linear-algebraic algorithms (Egger, 2022b). NMF works on TF-
IDF transformed data by breaking down a matrix into two lower-
ranking matrices (Obadimu et al., 2019). Specifically, TF-IDF is
a measure to evaluate the importance of a word in a collection
of documents. As demonstrated in Figure 2, NMF decomposes
its input, which is a term-document matrix (A), into a product
of a terms-topics matrix (W) and a topics-documents matrix
(H) (Chen et al., 2019). The values of Wand Hare modified
iteratively, where the former contains the basis vectors, and the
latter contains the corresponding weights (Chen et al., 2019). It is
necessary that all entries of Wand Hare non-negative; otherwise,
the interpretation of topics with negative values would be difficult
(Lee and Seung, 1999).
Since NMF requires the data to be preprocessed, necessary
steps to be performed beforehand include a classical NLP pipeline
containing, amongst others, lowercasing, stopword removal,
lemmatizing or stemming as well as punctuation and number
removal (Egger, 2022b). For this study, an open-source Python
library, Gensim, was used (Islam, 2019) to estimate the optimal
number of topics. By computing the highest coherence score, 10
topics could be identified.
Model 3: Top2Vec
Top2Vec (Angelov, 2020) is a comparatively new algorithm
that uses word embeddings. That is, the vectorization of text
data makes it possible to locate semantically similar words,
sentences, or documents within spatial proximity (Egger, 2022a).
For example, words like “mom” and “dad” should be closer
than words like “mom” and “apple.” In this study, a pretrained
embedding models, the Universal Sentence Encoder, was used
to create word and document embeddings. Since word vectors
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Egger and Yu A Topic Modeling Comparison
FIGURE 1 | Visual inspection of LDA.
FIGURE 2 | Intuition of NMF. Source: Egger (2022b).
that emerge closest to the document vectors seem to best
describe the topic of the document, the number of documents
that can be grouped together represents the number of topics
(Hendry et al., 2021).
However, since the vector space usually tends to be sparse
(including mostly zero values), a dimension reduction was
performed before density clustering. By using uniform manifold
approximation and projection (UMAP), the dimensions were
reduced to the extent that hierarchical density-based spatial
clustering of applications with noise (HDBSCAN) could be used
to identify dense regions in the documents (Angelov, 2020).
Finally, the centroid of the document vectors in the original
dimension was calculated for each dense area, corresponding to
the topic vector.
Notably, because words that appear in multiple documents
cannot be assigned to one single document, they were recognized
by HDBSCAN as noise. Therefore, Top2Vec does not require
any preprocessing (e.g., stopwords removal), or stemming
and lemmatization (Ma et al., 2021; Thielmann et al., 2021).
To conclude this model, Top2Vec automatically provided
information on the number of topics, topic size, and words
representing the topics.
Model 4: BERTopic
BERTopic (Grootendorst, 2020) builds upon the mechanisms
of Top2Vec; hence, they are similar in terms of algorithmic
structure. As the name suggests, BERT is used as an embedder,
and BERTopic provides document embedding extraction, with
a sentence-transformers model for more than 50 languages.
Similarly, BERTopic also supports UMAP for dimension
reduction and HDBSCAN for document clustering. The main
difference between Top2Vec is the application of a class-
based term frequency inverse document frequency (c-TF-IDF)
algorithm, which compares the importance of terms within a
cluster and creates term representation (Sánchez-Franco and
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Egger and Yu A Topic Modeling Comparison
FIGURE 3 | BERTopic’s interactive intertopic distance map.
Rey-Moreno, 2022). This means that the higher the value is for
a term, the more representative it is of its topic.
BERTopic, similar to Top2Vec, differs from LDA because
it provides continuous rather than discrete topic modeling
(Alcoforado et al., 2022). The stochastic nature of the model thus
leads to different results with repeated modeling. Once the model
is computed, researchers can output the most important topics.
Notably, Topic 0 with a count of1 will always represent outliers
and should not be considered any further. Researchers can also
search for a keyword and receive the most important topics based
on their similarity score along with the possibility to inspect
individual topics based on their keywords. Ultimately, in order
to better analyze the potentially large array of topics, BERTopic
offers an interactive intertopic distance map for inspecting
individual topics (Grootendorst, 2020). As illustrated in Figure 3,
once an initial overview of the topics becomes available, an
automated topic reduction can be performed again.
In essence, although topic models bring in statistical analysis
and can advance social science research, each of the algorithms
has its own uniqueness and relies on different assumptions.
Quantitative methods are limited in their ability to provide
in-depth contextual understanding, and the results cannot be
compared with any single “value” (Egger and Yu, 2021). Thus, the
interpretation of models still relies heavily on human judgment
(Hannigan et al., 2019) and researchers’ domain knowledge
(Egger and Yu, 2022).
In the following section, a comparison of the obtained results
will be divided into two parts, according to the nature of the
algorithm: (1) LDA and NMF and (2) Top2Vec and BERTopic.
The latter highlights the term search function as one of the
pros of using a guided/seeding approach to delve deeper into a
specific topic.
Comparison of LDA and NMF
Table 1 provides an overview of the 14 identified topics in the
LDA model and the 10 topics from NMF. Names were given
based on the terms that contributed the most to a topic in
reference to their TF-IDF weights. Overall, several aspects point
to common themes, such as expectations toward government
response, discussion on R(t) values, and travel restrictions in
different countries. Taking “government response” as an example,
tweets seem to focus on people’s expectations toward the White
House (e.g., #whcovidresponse) and the US president (#potus,
#vp). Although both models refer to the chance to reunite with
their loved ones (e.g., #loveisnottourism), LDA, in particular,
points out how the COVID-19 pandemic has influenced the
Diversity Visa Program (e.g., #dv2021) application. Likewise,
while both models disclose Twitter users’ opinions on travel
ban restrictions and quarantine, the LDA results appear to be
more geographically oriented. For instance, when discussing the
reproduction number, European countries, India, and the UK are
more frequently mentioned. On the other hand, England and
Scotland appear to be the main focal point concerning travel
restrictions, and as for tweets related to quarantine, LDA reveals
issues surrounding the Australian border.
Still, in spite of LDA performing seemingly better up to this
point, the model produces more universal and irrelevant topics
that, at the same time, barely offer any meaningful implications.
This can be evidenced from the final four LDA topics listed
in Table 1, which, based on the keywords, center on travel
and COVID-19 on a broader level. Therefore, despite the fact
that only a few NMF topics contain country-specific terms
(e.g., New Zealand, India, and the UK), its value should not
be underestimated. Due to a clear distinction between all the
identified topics in the NMF model, this research concludes
that the results obtained from NMF are more in line with
human judgment, thereby outperforming LDA in general. Yet,
as mentioned above, since topic extraction with LDA and NMF
relies primarily on hyperparameters, most of the results are
within expectation. As both models, however, do not allow for an
in-depth understanding of the phenomenon, the next section will
focus on the topic models that use embedding representations.
Comparison of BERTopic and Top2Vec
By relying on an embedding model, BERTopic and Top2Vec
require an interactive process for topic inspection. As such,
both algorithms allow researchers to discover highly relevant
topics revolving around a specific term for a more in-depth
understanding. Using Top2Vec for demonstration purposes,
this section begins with the intuition behind the search query.
Presuming that there is an interest in topics related to the
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Egger and Yu A Topic Modeling Comparison
TABLE 1 | Topics identified by LDA and NMF.
No. Topic/content Keywords Topic/content Keywords
1 Government response ban, travelgov, potus, dv2021,
loveisnottourism, whcovidresponse, end, visa,
please, vp
Government response whcovidresponse, potus,
loveisnottourism, cdcdirector, presssec,
vp, cdctravel, cdcgov, liftthetravelban,
cdctravel cdcdirector
2 Association for Molecular
Pathology (AMP) / mask
and virus
amp, travel, come, spread, mask, place, follow,
stay, keep, virus
Association for Molecular
Pathology (AMP) / desire to
covid, travel, people, amp, want, covid
travel, time, travel covid, like, year
3 Rtvalue / India, UK, Europe rt, travel, country, India, uk, covid, government,
list, eu, news
Rtvalue rt, covid, travel, https, covid19, traveler, rt
ollysmithtravel, traveler, httpstco,
4 Travel restriction / England
and Scotland
travel, covid, restriction, city, team, England,
despite, event, expect, Scotland
Travel restriction restriction, travel restriction, covid travel,
covid19 travel, ease, covid restriction,
travel, lift, covid19 restriction, restriction lift
5 Vaccination / border
between Canada and the
vaccinate, covid19, international, traveler,
travel, vaccination, Canada, border, US, fully
Travel ban / India and UK ban, India, travel ban, travel India, uk, list,
country, ban travel, red, variant
6 Quarantine and lockdown /
traveler, day, quarantine, variant, allow, return,
lockdown, Australia, break, two
General about travel /
covid19, travel, covid19 travel,
international, travel covid19, country,
pandemic, international travel, vaccination,
7 COVID-19 cases / USA case, new, travel, health, state, tourism, public,
number, close, include
Vaccination and quarantine vaccinate, fully, fully vaccinate, vaccinate
covid19, traveler, vaccinate traveler,
traveler, quarantine, cdc, require
8 Flight / COVID-19 test test, travel, need, positive, covid, flight,
negative, air, take, airport
COVID-19 cases / New
case, new, covid case, covid19 case, new
case, rise, Zealand, New Zealand, report,
case covid19
9 Death / Florida covid, die, death, cause, florida, child, spike,
shoot, traveler002, flu
COVID-19 test test, covid test, negative, positive, test
travel, test positive, PCR, covid19 test,
day, result
10 China and USA travel, covid, call, china, business, 2020,
trump, usa, dr
Vaccination pass vaccine, covid19 vaccine, covid vaccine,
passport, vaccine passport, require,
vaccine travel, dose, mandate, vaccination
11 Unspecific I not, covid, vaccine, people, do, travel, get,
make, still, would
12 Unspecific II travel, may, covid, 2, please, 1, help, show, 3,
13 Unspecific III covid19, travel, due, pandemic, world, today,
first, update, coronavirus, safe
14 Unspecific IV covid, be, go, travel, time, get, want, one, year,
term “cancel” during COVID-19, the Top2Vec model produces
relevant outputs (topics) based on the order of their cosine
similarity (Ghasiya and Okamura, 2021). Specifically, cosine
similarity, ranging from 0 to 1, measures the similarity between
the search term and a topic. In the case of this research, out of 309
topics, the similarity of Topic 10 proved to be the highest [0.50],
followed by Topic 20 [0.37], Topic 7 [0.33], Topic 123 [0.32], and
Topic 57 [0.30].
Thereafter, the most important keywords for each individual
topic can be retrieved. For example, the keywords for Topic 10
include the following:
[“refund,” “booked,” “ticket,” “cancelled,” “tickets,” “booking,
“cancel,” “flight,” “my,” “hi,” “trip,” “phone,” “email,” “myself,” “hello,
“couldn,” “pls,” “having,” “guys,” “am,” “sir,” “supposed,” “hopefully,
“me,” “excited,” “postpone,” “so,” “days,” “dad,” “paid,” “option,
“customers,” “request,” “bihar,” “thanks,” “amount,” “due,” “waiting,
“to,” “got,” “back,” “impossible,” “service,” “hours,” “complete,
“before,” “wait,” “nice,” “valid,” “book”].
In order to acquire an overview of the importance of each
term, a word cloud can be produced for better visualization (see
Figure 4); but, ultimately, an inspection of individual tweets is
also highly recommended. For instance, the findings suggest that
document 20189 (tweets: “@PaytmTravel Flight - AI 380 dated 9th
April, 2020 (Canceled due to COVID). No Refund since then [. . . ]”)
has a similarity score of 0.8518. This information allows one
to gain deeper insights directly from the raw data. Meanwhile,
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Egger and Yu A Topic Modeling Comparison
FIGURE 4 | Example of a word cloud based on the term “cancel.”
in order to find more suitable keywords based on “cancel” for
even further analysis, words that are most similar can be output
with their similarity, such as “canceled [0.60],” “refund [0.49],
“booked [0.47],” “due [0.46],” and “ticket [0.43].”
Following the search process, a topic comparison between
Top2Vec and BERTopic could be established. This time, “flight”
and “travel bubble” were taken as other examples. Since cosine
similarity has previously been introduced, the following section
merely lists some of the keywords that facilitate topic naming.
As mentioned above, this is because the results require human
interpretation to make sense of the data (Hannigan et al., 2019).
Starting with “flight,Table 2 provides an overview, out of
the 343 identified topics, of the six most relevant ones taken
from BERTopic and five, out of 253, from Top2Vec. Overall,
Top2Vec topics appear to be more policy- and regulation-
oriented, focusing on pre-departure testing requirements (e.g.,
negative PCR test and full vaccination) in countries such as
Mexico, the Netherlands, and Canada. It also discusses the
government’s travel advice for public transport, such as in trains,
buses, and flights. For a more qualitative inspection, relevant
tweets can be reviewed; take, for example, “Kind attention dear
passengers traveling to [. . . ] Please follow COVID-19 norms at
the airport. Fly safe!” and “[My] flight [got] canceled by airlines
due to covid. Also my travel insurance premium wasted.” On the
other hand, topics identified by BERTopic are more related to
the nature of air transport. Specifically, common issues shared
on Twitter include the airline industry, flight routes, returning
home, transmission through air, and air travel associations.
Turning to “travel bubble,” both algorithms produced five
relevant topics, as presented in Table 3. In this case, the
BERTopic results seem to be more specific, with a clear
distinction on travel between Australia and New Zealand,
Singapore and Hong Kong, as well as Canada and Mexico. Other
issues center on travel passes and business travel. With regards
to Top2Vec, however, the results revealed a slight overlap. For
example, the travel bubble between Australia and New Zealand
is covered in four out of five topics; similarly, Singapore, Hong
Kong, and Taiwan are also mentioned several times. In addition,
Top2Vec produces topics with multiple aspects, which becomes
especially apparent in the third and fourth topics. The third topic
contains issues related to six different countries (i.e., Hong Kong,
Singapore, Australia, New Zealand, the UK, and the Philippines),
and the fourth includes quarantine regulations in eight countries
(i.e., Singapore, Australia, New Zealand, Taiwan, Hong Kong,
Korea, Hawaii, and Indonesia).
As a final note, when inspecting the keywords of BERTopic
and Top2Vec, despite the redundancy of some terms (e.g.,
“travel bubble” and “travelbubble,” as they are very close in the
same vector-space), they can, in fact, provide valuable insights,
especially for the process of topic naming. Mostly, the content of a
topic can be understood based on frequently-repeated keywords.
Moreover, regarding the logic of the algorithm, since BERTopic
and Top2Vec should not be preprocessed, conjunction words
(e.g., after, before to, from, at) are helpful for connecting the
context. However, a major drawback without preprocessing is
that (in)definite articles or be-verbs appearing in the keywords
lists are often meaningless in comprehending a topic.
Hierarchical Topic Reduction of Top2Vec
and BERTopic
Finally, it is worth noting that both Top2Vec and BERTopic
allow for hierarchical reduction. Echoing this study’s results, the
number of extracted topics tends to be relatively large, thereby
necessitating the need for intensive qualitative analysis. In order
to streamline the analysis, the algorithms offer the possibility
to reduce these topics further (Angelov, 2020). Starting with
Top2Vec, a hierarchical reduction down to 10 topics is typically
considered a good starting point to begin topic analysis. In the
case of this research, the 10 remaining clusters deducted from
the 253 original topics are presented in Table 4. Significantly,
the original vectors remain after topic reduction, meaning that
representative topics with keywords can still be sought after at
any time.
Turning to BERTopic, since some of the topics are close
in proximity, as could be observed in the intertopic distance
map (Figure 3), visualization and topic reduction would provide
a better understanding of how the topics truly relate to each
other. To reduce the number of topics, hierarchical clustering
was performed based on the cosine distance matrix between topic
embeddings. This study thus took 100 topics as an example to
provide an overview of how and to which extent topics can be
reduced (Figure 5). Level 0 of the dendrogram demonstrates how
similar topics (those with the same colors) have been clustered
together. For example, Topic 4 (vaccine passports) and Topic
8 (the NHS COVID-19 app) were grouped together because of
their adjacency. Correspondingly, Topic 6 (wearing face masks)
and Topic 96 (mask mandate) were treated as part of the same
cluster. In essence, a visualization as such can help researchers
to better comprehend the algorithm’s criteria by which topics
are organized. After reviewing the proposed topic structure,
researchers can then decide on a number of topics that also seem
to be more realistic in an interactive manner.
However, for both algorithms, the underlying meanings of
the topics are still subject to human interpretation. Nevertheless,
although the intuition is to provide the best possible results, an
optimal number of topics could not be established because most
of the topics overlap with one another and cover a mixture of two
to three different aspects. For instance, the results from Top2Vec
(Table 4) present five topics associated with the US Diversity
Visa program (e.g., dv, selectees fault, winners, an excuse, justice,
interview, the petition, exam) and several terms related to
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Egger and Yu A Topic Modeling Comparison
TABLE 2 | Topics identified by BERTopic and Top2Vec for “flight.”
BERTopic Top2Vec
No. Topic/content Examples of keywords Topic/content Examples of keywords
1 Airline industry air travel, airline, air travel is, airlines,
aviation, flights, the airline industry, the
airline, airline industry, flight
Negative PCR / vaccination and
hours before, pre-departure, negative
covid, all travelers, fully vaccinated, pcr,
quarantine, days, requirement, mandatory
2 Flight routes flights from, flights, direct flights, flights
from india, canada eyes policy, canada
eyes, india to canada, to canada, ban on
direct, as india covid19
White House Secretary Tests
Positive / travel guide from
governmental institution
secretary, simon, house, white, tested
positive, travel guidelines, cdc, mps,
travelers, to follow
3 (Unable) to return home /
australians, travel ban, fly home, fly home
from, who fly home, who fly, to australia,
australians who fly, covid travel ban, travel
Negative PCR / fully vaccinated
before departure / foreign
travelers / Mexico
negative covid, fully vaccinated, foreign
travelers, pre departure, hours before,
required to, before you, to enter, pcr,
4 COVID transmission through air the air, aerosols, droplets, air, airborne,
covid travels, through the air, virus travels,
how covid travels, covid travels through
Negative PCR / fully vaccinated
before departure / foreign
travelers / the Netherlands and
negative covid, departure, hours before,
international travelers, fully vaccinated,
biden, the united, requirement,
netherlands, canadians
5 Airports Authority of India (AAI) /
aai, airports, aai airports, airport, the
airport, flights, aai is, airports are, from aai,
air traffic
Follow travel guidelines on public
transport (train / bus / flight) /
seek help and more info
train, bus, while traveling, covid
appropriate, more information, to follow,
covid guidelines, mandatory, by air, please
6 Airport news news airport airtravel, airtravel covid19
covid19india, airport airtravel, airport
airtravel covid19, travelers news airport,
airtravel covid19, travel covid19, flight
travel covid19, air travel associations,
airports air
TABLE 3 | Topics identified by BERTopic and Top2Vec for “travel bubble.”
BERTopic Top2Vec
No. Topic/content Examples of keywords Topic/content Examples of keywords
1 Australia and New Zealand travel bubble, travel bubble with, the travel
bubble, australia travel bubble,
zealandaustralia travel bubble, new
zealandaustralia travel, zealand travel,
zealand travel bubble, bubble with
australia, after travel bubble
Australia and New Zealand /
quarantine hotel
sydney, victoria, queensland, australia,
hotel quarantine, nz, in hotel, quarantine
free, lockdown, auckland
2 Singapore and Hong Kong bubble, travel bubble, singapore, air travel
bubble, travel bubble is, bubble is,
singaporehong kong air, singaporehong
kong, breaking singaporehong kong, as
singapore battles
Australia and New Zealand /
Singapore / Taiwan / vaccinated
zealand, quarantine free, singapore, hotel
quarantine, 2 weeks, isolate, vaccinated
travelers, lockdown, melbourne, Taiwan
3 Travel pass travel pass, covid travel pass, eus covid
travel, eus covid, the eus covid, covid
travel, summer travel, travel passes, travel
passes as, launch covid travel
Hong Kong and Singapore /
Australia and New Zealand /
green list / vaccinated / UK /
hong kong, singapore, zero covid, taiwan,
green list, australia, vaccinated travelers,
philippines, zealand, business travel
4 Nonessential travel / Canada and
Mexico ferry / spread of
canada and mexico, on non-essential
travel, nonessential travel at, nonessential
travel, ferry crossings, crossings with
canada, ferry crossings with, land and
ferry, and ferry crossings, spread of
Quarantine free / Singapore /
Australia and New Zealand /
Taiwan / Hong Kong / Korea /
Hawaii / Indonesia
quarantine free, singapore, hk, auckland,
taiwan, korea, sydney, hawaii, indonesia,
vaccinated travelers
5 Business travel business travel, tourism, travel industry,
the travel industry, tourism industry, and
tourism, travel and tourism, and tourism
industry, travel and, tourism industry the
Singapore / Hong Kong /
Australia / Taiwan / fully
vaccinated / green list
taiwan, singapore, hong kong, business
travel, zealand, australia, fully vaccinated,
portugal, green list, israel
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TABLE 4 | Hierarchical topic reduction of Top2Vec.
No. Topic/content Examples of keywords
1 Diversity visa / Student life byron, selectees fault, bay, mask, are increasing, student, the flu, exams, forever,
first wave, take, traveling, covid positive, there, hands, rapidly, want, big, stop,
death, interstate, fucking, haven, market, transmission, covid appropriate, bihar,
to wear, short, exam, increasing
2 Diversity visa and visa petition / freedom /
international travel / COVID-19 curfew
the petition, sign, tests for, pcr covid, selectees fault, boris, ford, ontario, want,
curfew, premier, the airport, free, friend, trudeau, postpone, check out, rapidly,
pakistan, shot, uk, enjoy, stay at, true, thread, toronto, travel insurance,
international travel, normal, many countries, variants, overseas travel, freedom,
mps, interstate, red list, folks, canadians, reasons, province, bihar
3 Diversity visa / unvaccinated people /
vaccinate to prevent
selectees fault, centers for, di, disease, white, labor, fauci, economy, behavior,
million, not being, market, shame, europeans, kerala, americans, control, here
are, millions of, trump, unvaccinated, buy, weekend, make sure, oct, and
tourism, dv, jobs, to protect, shop, this weekend, of vaccination, concerns, for
your, air travel, next month, vaccines, open, to ease, political, millions, virus,
prevention, cover, plans to, science, mexico, tourism
4 Politicians (Grant Shapps, Justin Trudeau,
Biden, Trump, Anthony Fauci) / green list
countries / international travel for
vaccinated people / olympics / COVID-19
on vaccination, eu, covid certificate, requirement for, ban, borders to, biden,
grant, shapps, president, even worse, chinese, olympics, trudeau, european,
required for, digital, vaccinated travelers, fauci, many countries, justice,
vaccinated travelers, travel pass, visas, other countries, trump, the federal,
countries, australians, green list, law, infected, joe, the border, for fully, interstate
travel, europe, open, next month, covid passports
5 Pre-COVID and first wave / dreaming of
first wave, shelby, battle, solutions, simon, they find, the emergence, their
journey, countless, lives, future, someone, human, money, an excuse, traveling,
love, before covid, dose, happy, traveled, pfizer, from china, dream, together,
selectees fault, died of
6 Complaints toward the US Diversity Visa
Lottery program (COVID-19 as an excuse
for the delay or cancellation thereof)
an excuse, toolset, selectees fault, even worse, on vaccination, uganda, death,
justice, pcr tests, new cases, arabia, interview, the highest, united states, fun,
winners, crazy, for fully, for foreign, nepal, imple, clear, african, nigeria, business
travel, puerto rico, brexit, the airport, requiring, singapore
7 Yellow fever and COVID-19 vaccine /
Saudi Arabia / COVID-19 cases
saudi, astrazeneca, journey, arabia, stay safe, new cases, covid numbers, dose
of, nhs covid, wave, wear mask, got covid, yellow fever, pass, app, pre covid,
doctors, eastern
8 Travel Destinations / Prevention / Travel
dv, selectees fault, blaming, lanka, covid appropriate, rapidly, european,
solutions, union, they find, the emergence, winners, travel advisory, increase,
nepal, prevention, the delta, travel measures, covid cases, shelby, surge in, level,
do not, new cases, travel related, eu, probably, hawaii, postpone, indian, to
restrict, battle, florida, are increasing, rising covid, olympics, governor
9 Negative PCR test prior to departure / fully
vaccinated for international travel
proof of, departure, hours before, covid appropriate, as long, will need, covid
testing, negative covid, be fully, pre departure, to show, requirement for, you
must, required to, by air, foreign travelers, test for, covid test, behavior,
vaccinated against, test, pcr test, pcr tests, arrival, fully vaccinated, on
vaccination, requirement, of vaccination, negative test, pcr, vaccination,
negative, are fully, cdc, required, for international, requirements for, distancing, to
require, guidance, on arrival, days of
10 Travel bubble / Australia (several cities
included) and New Zealand / Hong Kong /
Scotland / quarantine free / quarantine
nsw, queensland, sydney, victoria, have tested, coast, shelby, melbourne, travel
bubble, zealand, quarantine free, australia, in hotel, positive for, simon, wales,
traveled from, kong, covid case, positive covid, battle, tested positive, first wave,
vic, greater, auckland, woman, their journey, byron, the petition, hotel quarantine,
scotland, south, army
politicians based in the USA and Canada (e.g., Grant Shapps,
Justin Trudeau, Joe Biden, Donald Trump, Anthony Fauci).
Similarly, making sense of the hierarchical clustering produced
by BERTopic (Figure 5) also requires an enormous effort since
the topic structure changes whenever researchers experiment
with a different number of topics. Despite the possibility of using
existing domain know-how to search for specific topics, a feature
that is inexistent in other traditional algorithms, researchers
should be well aware of the aforementioned issues. The overall
process contains errors, and it may be quite labor-intensive to
find a number that fits human judgment.
As shown in Figure 5 below, the dendrogram produced by
BERTopic shows the agglomeration levels of the individual
topics. This visualization, in particular, aids in finding an
appropriate number of k-topics. Furthermore, similar to
Top2Vec, a table with keywords is obtained after fusing the
topics; yet, it is also highly recommended to inspect individual
raw documents for more appropriate interpretations.
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Egger and Yu A Topic Modeling Comparison
FIGURE 5 | Hierarchical reduction in BERTopic.
Baring the difficulties of extracting useful information from
short and unstructured texts in mind, this research intends
to confront such challenges by comparing the results of four
topic modeling algorithms. For an overall evaluation based
on human interpretation, this study supports the potency
of BERTopic and NMF, followed by Top2Vec and LDA, in
analyzing Twitter data. While, in general, both BERTopic and
NMF provide a clear cut between any identified topics, the
results obtained from NMF can still be considered relatively
“standard.” Contrarily, in addition to the expected outcomes (i.e.,
topics), BERTopic was able to generate novel insights using its
embedding approach. Although Top2Vec also uses pretrained
embedding models, the results cover more topics that overlap and
contain multiple concepts. On the other side of the spectrum,
similar to NMF, the topics produced by LDA do not seem to
be very intriguing, either. Thus, despite some Top2Vec topics
appearing as irrelevant and difficult to understand, the model,
even so, is capable of producing a few interesting findings rarely
mentioned by other algorithms (e.g., politicians). As a result, in
favor of extracting novel conclusions, this research recommends
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Egger and Yu A Topic Modeling Comparison
Top2Vec over LDA. To provide a more solid foundation for these
reasonings, a detailed evaluation for each algorithm will now
be given.
First and foremost, compared to other techniques, BERTopic
works exceptionally with pretrained embeddings (Sánchez-
Franco and Rey-Moreno, 2022) due to a split between
clustering the documents and using c-TF-IDF to extract topic
representations. Especially owing to the c-TF-IDF procedure
(Abuzayed and Al-Khalifa, 2021), BERTopic can support several
topic modeling variations, such as guided topic modeling,
dynamic topic modeling, or class-based topic modeling. Its
main strength lies in the fact that the algorithm performs
well on most aspects of the topic modeling domain, whereas
others typically excel in one single aspect. Additionally,
after having trained a BERTopic model, it is also possible
to reduce the number of topics (Sánchez-Franco and Rey-
Moreno, 2022), subsequently allowing researchers to settle
on a number of (realistic) topics based on how many were
actually produced.
Slightly different from BERTopic and the implementation of
c-TF-IDF, Top2Vec creates jointly embedded word, document,
and topic vectors to find topic descriptions (Angelov, 2020). The
intuition behind this algorithm is that every input is considered a
vector, and pivoting between them is trivial. Hence, Top2Vec can
scale a large number of topics and vast quantities of data. Such
strength is especially required when multiple languages emerge
within a corpus (Hendry et al., 2021). The main disadvantage of
Top2Vec, however, is that it is unqualified to work with a small
amount of data (Abuzayed and Al-Khalifa, 2021; e.g., <1,000
documents). In fact, BERTopic and Top2Vec have a number
of issues in common. For example, although outlier generation
might be beneficial in some cases, the solutions might actually
generate more outliers than expected. Meanwhile, another flaw
involves topic distributions: they cannot be generated within a
single document because each document is assigned to a single
topic. Although probabilities can indeed be extracted, they are
not equivalent to an actual topic distribution.
With regards to NMF and LDA, notwithstanding that both
algorithms do not require social scientists to have prior domain
knowledge, several topics identified by LDA in this study yielded
either universal (Rizvi et al., 2019) or irrelevant (Alnusyan
et al., 2020) pieces of information. Such an issue further reflects
the study’s findings of LDA being indeterministic (Egger and
Yu, 2021). In order to achieve optimal results, LDA usually
requires detailed assumptions concerning the hyperparameters;
in particular, discovering the optimal number of topics typically
proves to be a difficult task (Egger and Yu, 2021). Although
NMF shares the same disadvantages, it can be assumed that
NMF puts forward better results since the algorithm relies on
TF-IDF weighting rather than raw word frequencies (Albalawi
et al., 2020). Simultaneously, as a linear-algebraic model, scholars
commonly agree that NMF works well with shorter texts (Chen
et al., 2019), such as tweets. Since no prior knowledge is
needed for topic extraction (Albalawi et al., 2020), this strength
specifically benefits research based on social media data (Blair
et al., 2020). Additionally, as LDA extracts independent topics
from word distributions, topics that are deemed dissimilar in
the document may not be identified separately (Campbell et al.,
2015), thereby resulting in overlapping clusters (Passos et al.,
2011). In opposition, other scholars believe that insufficient
statistical information for feature extraction is the fundamental
factor behind duplicate topics (Cai et al., 2018).
Lastly, when comparing BERTopic to NMF, a major
shortcoming of NMF revolves around its low capability to
identify embedded meanings within a corpus (Blair et al.,
2020). Considering that the algorithm depends primarily on the
Frobenius norm (Chen et al., 2019), which is typically useful for
numerical linear algebra, this issue ultimately leads to difficulties
in interpreting findings (Wang and Zhang, 2021). Though NMF
can effectively analyze noisy data (Blair et al., 2020), others argue
that accuracy cannot be guaranteed (Albalawi et al., 2020).
Based on the outcomes of this study, as discussed above,
Table 5 summarizes the pros and cons of applying LDA,
NMF, BERTopic, and Top2Vec in order to help facilitate
social scientists in the necessary preprocessing steps, proper
hyperparameter tuning, and comprehensible evaluation of their
results. However, researchers should take into account that,
depending on the nature of the datasets, topic models may not
always perform in the same fashion (Egger and Yu, 2021).
Theoretical and Practical Contributions
In light of the expansion of user-generated content, social media
has broadened the horizons for human interaction and provoked
new phenomena and social research for further investigation
(Murthy, 2012; Rizvi et al., 2019; Boccia Artieri et al., 2021).
Although several recent studies have vouched for the exploration
of short-text social media data (Albalawi et al., 2020; Qiang et al.,
2020), existing knowledge is rather restricted to conventional
modeling techniques such as LDA and LSA (Albalawi et al.,
2020). As the evolution of topic modeling has given rise to
novel techniques, especially ones that have rarely been applied or
evaluated in social science, this study is valuable in that it answers
the call to assess topic modeling via a thorough comparison of
four different algorithms (Reisenbichler and Reutterer, 2019). In
addition, this research scrutinizes the bright and dark sides of
applying embedded vs. standard topic models, but it also offers
social science researchers insights into methodological challenges
that may hinder knowledge generation.
Foreseeing that social scientists may indeed hesitate to choose
an appropriate algorithm when analyzing social media data, this
study presents possible methodological issues and promotes the
efficacy of two different types of topic models. To be more
precise, applying BERTopic to generate insights from short and
unstructured text offers the most potential when it comes to
embedding-based topic models. Thus, this study acknowledges
the capability of BERTopic to encode contextual information
(Chong and Chen, 2021), an aspect that may remain concealed
by other models. Regarding traditional topic model algorithms,
social science research is encouraged to consider NMF as an
alternative approach to the commonly-adopted LDA (Gallagher
et al., 2017). Certainly, however, it is essential to note that each
model has its own strengths and shortcomings, and the findings
require intensive qualitative interpretation. Finally, this study
also strives to make another important contribution by outlining
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Egger and Yu A Topic Modeling Comparison
TABLE 5 | Comparison of topic models.
Advantages Disadvantages
LDA Prior domain knowledge is not necessarily required
Finds coherent topics when correct hyperparameter
tuning is applied
Can deal with sparse input
The number of topics is generally smaller than
word-embedding based approaches; thus, it is easier
to be interpreted
One document can contain several different topics
(Mixed membership extraction)
Full generative models with multinominal distribution
over topics are generated
Shows both adjectives and nouns within topics
Detailed assumptions are required
Hyperparameters need to be tuned carefully
• Results can easily produce overlapping topics as topics are soft
Objective evaluation metrics are widely missing
The number of topics needs to be defined by the user(s)
Since the results are not deterministic, reliability and validity are not
automatically ensured
Assumes that the topics are independent of each other; hence, only
the frequency of the common occurrence of words is used
Word correlations are ignored, so no relationships between topics
can be modeled
NMF Prior domain knowledge is not required
Supports mixed membership models; thus, one
document can contain several topics
In contrast to LDA, which uses raw word frequencies,
the term-document matrix can be weighted with
It proves to be computationally efficient and very
Easy to implement
Frequently delivers incoherent topics
The number of topics to be extracted must be defined by the user in
Implicit specification of probabilistic generative models
Top2Vec Supports hierarchical topic reduction
Allows for multilingual analysis
Automatically finds the number of topics
Creates jointly embedded word, document, and topic
Contains built-in search functions (easy to go from
topic to documents, search topics, etc.)
Can work on very large dataset sizes
It uses embeddings, so no preprocessing of the
original data is needed
The embedding approach might result in too many topics, requiring
labor-intensive inspection of each topic
Generates many outliers
Not very suitable for small datasets (<1,000)
Each document is assigned to one topic
Objective evaluation metrics are missing
BERTopic High versatility and stability across domains
Allows for multilingual analysis
Supports topic modeling variations (guided topic
modeling, dynamic topic modeling, or class-based
topic modeling)
It uses embeddings, so no preprocessing of the
original data is needed
Automatically finds the number of topics
Supports hierarchical topic reduction
Contains built-in search functions (easy to go from
topic to documents, search topics, etc.)
Broader support of embedding models than Top2Vec
The embedding approach might result in too many topics, requiring
labor-intensive inspection of each topic
Generates many outliers
No topic distributions are generated within a single document; rather,
each document is assigned to a single topic
Objective evaluation metrics are missing
guided modeling solutions that can be applied by social scientists
to data analytics for knowledge extraction.
Limitations and Recommendations for
Future Research
This research is certainly not without its limitations. While this
study responds to a need to utilize Top2Vec and BERTopic for
the analysis of short-text data (Egger and Yu, 2021; Sánchez-
Franco and Rey-Moreno, 2022), novel language models, such as
GPT3 and WuDao 2.0, have continued to emerge as time passes
(Nagisetty, 2021), thereby acting as an excellent basis for even
more powerful topic modeling approaches. To leverage the use
of topic modeling methods, social scientists are encouraged to
try and evaluate other newly developed algorithms and to keep
their knowledge up to date. In the case of this study, Twitter was
selected due to its strict regulations on the number of characters
allowed per tweet, making it an ideal platform for exploratory
research. Nonetheless, the methodological approach in this study
should be applicable to other channels as well since social media
posts, in general, are short and unstructured (Kasperiuniene
et al., 2020). However, it is still critical to note that the nature
of social media differs in terms of user demographics, text
presentation, or rhetoric, amongst others. Thus, future research
should continue to explore the effectiveness of topic modeling
algorithms across other platforms. Lastly, acknowledging the
epistemological challenges of big data is also of importance;
regardless of the massive volumes of data that may appear
tempting at face value, algorithms should be contextualized in
a particular social framework (Egger and Yu, 2022). Although
topic models have quantified short-text social media data, both
Frontiers in Sociology | 13 May 2022 | Volume 7 | Article 886498
Egger and Yu A Topic Modeling Comparison
the interpretation and justification of the results come at the
expense of data accuracy. Being equipped with extensive domain
knowledge in data-driven science (Canali, 2016) would therefore
allow social scientists to transform quantitative analytics into
valuable insights for knowledge acquisition.
The original contributions presented in the
study are included in the article/supplementary
material, further inquiries can be directed to the
corresponding author.
RE collected and analyzed the data. JY wrote the manuscript
in consultation with RE and interpreted the data. Both authors
designed the study and were responsible for the overall
management and planning. All authors contributed to the article
and approved the submitted version.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
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Frontiers in Sociology | 16 May 2022 | Volume 7 | Article 886498
... For example, a key drawback of the LDA model is the possible overlap of topics. The problem with BERTopic is that each document can only be assigned to a single topic [20]. ...
... In addition, the number of topics is smaller than in topic-embedding models, which is important for easier text interpretation. The disadvantage is the neglect of word correlation, and the results may generate overlapping topics [20]. ...
... There are several articles comparing the results of LDA and BERTopic modeling in different scientific fields. Most authors agree that the BERTopic model often shows better results than the LDA model [20,54]. The BERTopic not only shows more interpretable results, but also does not require preprocessing of data, and consumes fewer computer resources [54]. ...
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The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields.
... No entanto, métodos tradicionais de modelagem de tópicos enfrentam dificuldades ao tentar identificar os assuntos tratados em textos curtos, principalmente pela falta de coocorrência de palavras [Costa and Duarte 2019]. Mais recentemente, novas abordagens foram desenvolvidas que se apóiam na semântica distribucional e na utilização de vetores densos de palavras para compor tópicos [Egger and Yu 2022]. Entretanto, problemas como a falta de coocorrência de palavras, ausência de informações nos textos e o uso de termos inseridos apenas no contexto das redes sociais ainda se mostram como desafios [Qiang et al. 2020]. ...
... O NMF usa uma abordagem deálgebra linear para extração de tópicos. [Egger and Yu 2022] avaliam e comparam o desempenho de quatro técnicas de modelagem de tópicos: LDA, NMF, Top2Vec e BERTopic. Além dos métodos já citados anteriormente, o Top2Vec usa embeddings para representar os textos curtos. ...
... Ao analisar os trabalhos apresentados nessa seção, percebemos que alguns autores elaboraram um estudo exploratório de métodos específicos para textos curtos [Qiang et al. 2020, Costa andDuarte 2019], enquanto outros focaram em avaliar o desempenho de métodos de modelagem de tópicos para textos genéricos, mas aplicados no contexto de textos curtos [Albalawi et al. 2020]. Por fim, as abordagens apresentadas verificaram o desempenho apenas de métodos de textos genéricos com e sem o uso de embeddings [Egger and Yu 2022]. Nenhum dos trabalhos relacionados explorou o que este artigo propõe: uma análise comparativa entre métodos tradicionais (LDA), modelos para textos curtos (GSDMM e PTM) e novas abordagens para textos genéricos com o uso de embeddings (BERTopic). ...
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As redes sociais são utilizadas para expressar opiniões ou interagir com outras pessoas. Diante do amplo escopo de assuntos publicados e a linguagem informal presente nas postagens, a busca de informações é significativamente desafiadora. Assim, descobrir automaticamente os tópicos tratados nos textos ruidosos e com pouco contexto postados é primordial. Dado este cenário, este artigo contribui com uma análise comparativa de métodos de modelagem de tópicos, incluindo os baseados em abordagens probabilísticas e neurais. Ademais, esse artigo contribui com um método para rotular automaticamente os tópicos, permitindo uma análise qualitativa dos tópicos descobertos.
... The 17 SDGs, see description in [1], are as follows (those most closely related to Aerospace Engineering have been written in italic font): ...
... To take advantage of this fact, ASDG introduces a voting mechanism. Similar ideas have been used very recently for studying the social network Twitter [17]. In the voting stage, ASDG takes the scores of each model for each text as inputs. ...
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The 2030 Agenda of the United Nations (UN) revolves around the Sustainable Development Goals (SDGs). A critical step towards that objective is identifying whether scientific production aligns with the SDGs' achievement. To assess this, funders and research managers need to manually estimate the impact of their funding agenda on the SDGs, focusing on accuracy, scalability, and objectiveness. With this objective in mind, in this work, we develop ASDG, an easy-to-use artificial-intelligence (AI)-based model for automatically identifying the potential impact of scientific papers on the UN SDGs. As a demonstrator of ASDG, we analyze the alignment of recent aerospace publications with the SDGs. The Aerospace data set analyzed in this paper consists of approximately 820,000 papers published in English from 2011 to 2020 and indexed in the Scopus database. The most-contributed SDGs are 7 (on clean energy), 9 (on industry), 11 (on sustainable cities) and 13 (on climate action). The establishment of the SDGs by the UN in the middle of the 2010 decade did not significantly affect the data. However, we find clear discrepancies among countries, likely indicative of different priorities. Also, different trends can be seen in the most and least cited papers, with clear differences in some SDGs. Finally, the number of abstracts the code cannot identify is decreasing with time, possibly showing the scientific community's awareness of SDG.
... In contrast to LDA and LSA, NMF is a decompositional, non-probabilistic algorithm using matrix factorization. It works on TF-IDF-transformed data by breaking down a matrix into two secondary matrices [24]. BERTopic is a topic-modeling technique that leverages transformers and c-TF-IDF to form dense clusters, allowing for explicable topics while keeping important words within the topic descriptions. ...
... BERTopic supports guided, (semi-)supervised, and dynamic topic modeling [25]. Extant research demonstrates that BERTopic can provide more clear-cut topics and generate more novel insights than LSA and LDA with respect to short texts [24]. Filieri et al. [26] employed LSA to separately extract critical service features from positive and negative online reviews. ...
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In the Web2.0 era, user-generated content (UGC) provides a valuable source of data to aid in understanding consumers and driving intelligent business. Text mining techniques, such as semantic analysis and sentiment analysis, help to extract meaningful information embedded in UGC. However, research on text mining of UGC for e-commerce business applications involves interdisciplinary knowledge, and few studies have systematically summarized the research framework and application directions of related research in this field. First, based on e-commerce practice, in this study, we derive a general framework to summarize the mainstream research in this field. Second, widely used text mining techniques are introduced, including semantic and sentiment analysis. Furthermore, we analyze the development status of semantic analysis in terms of text representation and semantic understanding. Then, the definition, development, and technical classification of sentiment analysis techniques are introduced. Third, we discuss mainstream directions of text mining for business applications, ranging from high-quality UGC detection and consumer profiling, to product enhancement and marketing. Finally, research gaps with respect to these efforts are emphasized, and suggestions are provided for future work. We also provide prospective directions for future research.
... Besides BERT, BERTopic also supports several other word extraction models such as XLM-R which supports more than 50 languages for text extraction other than English. In addition, several advantages of BERTopic that can support this research are that BERTopic supports a variety of topic modeling, hierarchical topic reduction, and can find the number of topics automatically [27]. ...
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Technology and information growth make all internet users can play a role in disseminating information, including hoax news. One way that can be done to avoid hoax news is to look for sources of information, but valid news is not always perceived as 'true' by individuals because human judgments can lead to bias. Several studies on automatic hoax news classification have been carried out using various deep learning approaches such as the pre-trained multilingual transformer model. This study focuses on classifying Indonesian hoax news using the pre-trained transformer multilingual model (XLM-R and mBERT) combined with a BERTopic model as a topic distribution model. The result shows that the proposed method outperforms the baseline model in classifying fake news in the low-resource language (Indonesian) with accuracy, precision, recall, and F1 results of 0.9051, 0.9515, 0.8233, and 0.8828 respectively.
... Each topic modeling technique has its strengths and weaknesses. We preferred BERTopic due to its increased performance on short and unstructured text such as Twitter data as shown in earlier studies [47,48]. ...
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The Flint Water Crisis (FWC) was an avoidable public health disaster that has profoundly affected the city’s residents, a majority of whom are Black. Although many scholars and journalists have called attention to the role of racism in the water crisis, little is known about the extent to which the public attributed the FWC to racism as it was unfolding. In this study, we used natural language processing to analyze nearly six million Flint-related tweets posted between April 1, 2014, and June 1, 2016. We found that key developments in the FWC corresponded to increases in the number and percentage of tweets that mentioned terms related to race and racism. Similar patterns were found for other topics hypothesized to be related to the water crisis, including water and politics. Using sentiment analysis, we found that tweets with a negative polarity score were more common in the subset of tweets that mentioned terms related to race and racism when compared to the full set of tweets. Next, we found that word pairs that included terms related to race and racism first appeared after the January 2016 state and federal emergency declarations and a corresponding increase in media coverage of the FWC. We conclude that many Twitter users connected the events of the water crisis to race and racism in real-time. Given growing evidence of negative health effects of second-hand exposure to racism, this may have implications for understanding minority health and health disparities in the US.
... Non-Negative Matrix Factorization (NMF) (Lee and Seung, 1999;Xu et al., 2003;Gillis, 2014) is used to detect latent semantic topics and assign papers to topics; we chose NMF because it is a simple and fast method experimentally shown to generate more stable and interpretable topics, especially on shorter text, compared to other topic model such as LDA (Chen et al., 2019;Egger and Yu, 2022) ...
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This paper proposes a text-mining framework to systematically identify vanishing or newly formed topics in highly interdisciplinary and diverse fields like cognitive science. We apply topic modeling via non-negative matrix factorization to cognitive science publications before and after 2012; this allows us to study how the field has changed since the revival of neural networks in the neighboring field of AI/ML. Our proposed method represents the two distinct sets of topics in an interpretable, common vector space, and uses an entropy-based measure to quantify topical shifts. Case studies on vanishing (e.g., connectionist/symbolic AI debate) and newly emerged (e.g., art and technology) topics are presented. Our framework can be applied to any field or any historical event considered to mark a major shift in thought. Such findings can help lead to more efficient and impactful scientific discoveries.
... It has also been applied to the theme of COVID-19 to determine the main pandemic health effects [34] and the public sentiment toward vaccination [22]. Compared to LDA, which gives more general descriptions of broader topics [46], the architecture of NMF enables it to find more detailed, clear-cut, and coherent topics [37,46,47]. Chen et al [18] even claim that NMF can learn from data similarly to the way humans do, which makes its results more easily interpretable than in the case of LDA. ...
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Background: Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (such as Twitter, Reddit, and Facebook) for user-created content from which they could gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses negative sentiment toward vaccination, it can give a direct insight into reasons for vaccine hesitancy. Objective: This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods: To study the attitudes and beliefs behind vaccine hesitancy, we collected two batches of tweets that mention some aspects of the COVID-19 vaccination. The first batch of 8,817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant were annotated as positive, negative or neutral. We used the annotated tweets to train a sequential BERT-based classifier for two tweet classification tasks to augment this initial dataset. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined datasets resulted in 3,286 tweets with a negative sentiment: 1,770 from the manually annotated dataset and 1,516 as a result of automatic classification. Topic modeling methods (Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) were applied using 3,286 preprocessed tweets to detect reasons for vaccine hesitancy. Results: The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85 and 0.85 for negative neutral and positive sentiment, respectively. By summarizing the topics obtained in both models, we extracted five main groups of reasons for vaccine hesitancy: Concern over vaccine side effects, Concern over vaccine effectiveness, Concern over insufficiently tested vaccines, Mistrust of authorities and Conspiracy theories. Conclusions: This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process. Clinicaltrial:
... Specifically, we looked at the frequency of post publishing. Then, we analyzed the thematic composition of these posts using BERTopic (Grootendorst, 2022), a novel topic modeling technique that supports dynamic topic modeling for multilingual text corpora and demonstrates high performance across different domains (Egger & Yu, 2022). We chose topic modeling for the analysis of frames since this method offers an advantage of mining emerging frames from a body of texts rather than imposing pre-determined classification categories on the data set, and is thus especially useful in fast-changing and previously unexplored contexts (Groshek & Engelbert, 2013) such as the one we focus on. ...
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On 24 February 2022, Russia launched a large-scale invasion of Ukraine which since then has been consistently referred to by the Russian authorities as a special operation and not war. The purpose of this specific framing of the Russian aggression can be attributed to multiple reasons, but among the main advantages it provides to the Kremlin is the emphasis of power inequality between Russia and Ukraine as well as minimizing the concerns of the Russian population about the impact of the war on their lives. However, despite all the Kremlin's propaganda and censorship efforts, it is still unclear what the dominant framing of the war is among regular Russian internet users and how they engage with different frames used in relation to it. To address this gap, we conducted an automated analysis of over 6 million public posts from VK, the biggest Russian social media platform, published between 14 February 2022 and 24 June 2022 and related to different ways of framing the war as well as the ways users engage with these frames. Our analysis reveals that the invasion was predominantly discussed in connection with the losses in the Russian army, and allusions to WW2 were also common. The users who posted about the war were older than average VK users and tuned in to pro-regime and pro-war content more frequently.
Twitter was widely used during the 2020 U.S. election to disseminate claims of election fraud. As a result, a number of works have examined this phenomenon from a variety of perspectives. However, none of them focus on analyzing topics behind the general fraud claims and associating them with user communities. To fill this gap, we propose to uncover and characterize groups of Twitter users engaging in discussions about election fraud claims during the 2020 U.S. election using a large dataset that spans seven weeks during this period. To accomplish this, we model a sequence of co-retweet networks and employ a backbone extraction method that controls for inherent traits of social media applications, particularly, user activity levels and the popularity of tweets (which together generate many spurious edges in the network), thus allowing us to reveal topics of tweets that lead users to retweet them. After extracting the backbones, we identify user groups representative of the communities present in the network backbones and finally analyze the topics behind the retweeted tweets to understand how they contributed to the spread of fraud claims at that time. Our main results show that (i) our approach uncovers better-structured communities than the original network in terms of users spreading discussions about fraud; and (ii) these users discuss 25 topics with specific psycholinguistic and temporal characteristics.
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The needs of travellers vary across cultures. When it comes to culinary aspects, there is a strong connection between gastronomy and culture. To optimise service offerings, investigation of the essential aspects of dining experiences in relation to cultural backgrounds is of great importance. In the age of digitalisation, tourists share their dining experiences throughout their multiphasic travel journey via online platforms. By considering nine distinct cultural backgrounds, this research aims to investigate tourist experiences based on TripAdvisor restaurant reviews through topic modelling, using the city of Salzburg as its study context. Depending on one’s cultural circumstances, the findings demonstrate that the most important aspects include staff, food-menu items, value for money, restaurant physical appearance, food authenticity, overall service, menu offers, food quality, atmosphere, and recommendations. This study advances the state-of-the-art knowledge of societal culture as a variable in the target market analysis of restaurant customers. Findings allow restaurant owners, other tourism service providers, and destination management organisations to analyse and adapt their service offerings and strategies accordingly.
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The crisis arising from the COVID-19 pandemic has affected the entire tourism system, including communication and marketing practices. Of these practices, in recent years influencer marketing has been one of the more successful strategies for both destinations and tourism businesses. This research investigates the impacts of the COVID-19 crisis on travel content creators, their communicative practices, and their engagement with audiences, brands and health authorities. The study uses netnography based on immersion, interviews and social media content analysis. The results obtained show that the pandemic has transformed influencer marketing and has driven influencers to change their business strategies, content creation tactics and engagement mechanisms. The findings contribute to the crisis communication literature by illustrating that influencers constitute important allies for organisations when communicating during a crisis and have played a critical role in tourism recovery.
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Our research applies a service, feature-oriented approach to deeply explore the subjective experiences shared publicly by Airbnb guests in their reviews. Our processed data set contains 73,557 reviews of Airbnb stays in coastal and urban destinations between 2017 and 2020. A topic modeling based on the BERTopic approach is applied to detect dense clusters of reviews and identify one highly relevant and interpretable topic per cluster related to core and essential sharing services and surrounding features. Our study, therefore, allows a higher understanding of the relationships between urban versus coastal destinations and guests' preferences. Furthermore, it enables hosts to differentiate the touristic short-rentals lodgings according to customer experiences.
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Purpose: Intrigued by the methodological challenges emerging from text complexity, this study evaluates the effectiveness of different topic modelling algorithms based on Instagram textual data. Design/methodology/approach: By taking Instagram posts captioned with #darktourism as the study context, this research applies latent Dirichlet allocation (LDA), correlation explanation (CorEx), and non-negative matrix factorisation (NMF) to uncover tourist experiences. Findings: CorEx outperforms LDA and NMF by classifying emerging dark sites and activities into 17 distinct topics. The results of LDA appear homogeneous and overlapping, whereas the extracted topics of NMF are not specific enough to gain deep insights. Originality: This study assesses different topic modelling algorithms for knowledge extraction in the highly heterogeneous tourism industry. The findings unfold the complexity of analysing short-text social media data and strengthen the use of CorEx in analysing Instagram content.
Nonnegative matrix factorization (NMF) based topic modeling methods do not rely on model- or data-assumptions much. However, they are usually formulated as difficult optimization problems, which may suffer from bad local minima and high computational complexity. In this paper, we propose a deep NMF (DNMF) topic modeling framework to alleviate the aforementioned problems. It first applies an unsupervised deep learning method to learn latent hierarchical structures of documents, under the assumption that if we could learn a good representation of documents by, e.g. a deep model, then the topic word discovery problem can be boosted. Then, it takes the output of the deep model to constrain a topic-document distribution for the discovery of the discriminant topic words, which not only improves the efficacy but also reduces the computational complexity over conventional unsupervised NMF methods. We constrain the topic-document distribution in three ways, which takes the advantages of the three major sub-categories of NMF—basic NMF, structured NMF, and constrained NMF respectively. To overcome the weaknesses of deep neural networks in unsupervised topic modeling, we adopt a non-neural-network deep model—multilayer bootstrap network. To our knowledge, this is the first time that a deep NMF model is used for unsupervised topic modeling. We have compared the proposed method with a number of representative references covering major branches of topic modeling on a variety of real-world text corpora. Experimental results illustrate the effectiveness of the proposed method under various evaluation metrics.
Traditional text classification approaches often require a good amount of labeled data, which is difficult to obtain, especially in restricted domains or less widespread languages. This lack of labeled data has led to the rise of low-resource methods, that assume low data availability in natural language processing. Among them, zero-shot learning stands out, which consists of learning a classifier without any previously labeled data. The best results reported with this approach use language models such as Transformers, but fall into two problems: high execution time and inability to handle long texts as input. This paper proposes a new model, ZeroBERTo, which leverages an unsupervised clustering step to obtain a compressed data representation before the classification task. We show that ZeroBERTo has better performance for long inputs and shorter execution time, outperforming XLM-R by about 12% in the F1 score in the FolhaUOL dataset. KeywordsLow-resource NLPUnlabeled dataZero-shot learningTopic modelingTransformers
Today, a vast amount of unstructured text data is consistently at our disposal. Owing to the rapid increase in user-generated content on the one hand and powerful, ready-to-use machine learning algorithms on the other hand, automated text analysis can now be carried out in a way that would have been unimaginable a few years ago. However, in order to be able to analyze textual data further, it first becomes necessary to extract features and transform the text into numerical values, which is required by many algorithms. As such, this chapter will present the basics of vectorizing text, beginning with the most simple approaches of text representation and increasing in complexity and performance as each subsequent algorithm is presented. The aim of this chapter is, thus, to convey the intuition behind each approach in a straightforward manner and to focus on the elements that are relevant for practical application.
Due to the rapid growth of texts in today’s society, much of which is produced via online social networks in the form of user-generated content, extracting useful information from unstructured text poses quite a challenge. However, thanks to the rapid development of natural language processing algorithms, including topic modelling techniques that help to discover latent topics in text documents such as online reviews or Twitter and Facebook posts, this challenge can be confronted. As such, topic modelling approaches have been gaining popularity in the field of tourism; yet, often little insight is given into the creation process and the quality of topic modeling results. Thus, this chapter aims to introduce several topic modelling algorithms, to explain their intuition in a brief and concise manner, and to provide tips and hints in relation to the necessary (pre-) processing steps, proper hyperparameter tuning, and comprehensible evaluation of the results.
Epistemology is concerned with the preconditions and goals of knowledge, helping us to understand how knowledge is generated. However, in recent years, the availability of Big Data and the advancement of Data Science (DS) methods have led to heated discussions as well as to a divided scientific community. On the one hand, there are those who believe that knowledge can now be generated exclusively from data, rendering theory no longer necessary, while the opposing voices, on the other hand, do not want to rely solely on the results that algorithms extract from the data. As such, this chapter discusses both perspectives and warns against taking a one-sided position. Rather, the goal is to understand the advantages of both sides and to profit from the synergies between the two scientific approaches.