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Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey

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Hamed Jelodar at University of New Brunswick
Hamed Jelodar
Yongli Wang at Nanjing University of Science and Technology
Yongli Wang
Chi Yuan
Chi Yuan
Chi Yuan
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Xia Feng
Xia Feng
Xia Feng
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Abstract and Figures

Topic modeling is one of the most powerful techniques in text mining for data mining, latent data discovery, and finding relationships among data and text documents. Researchers have published many articles in the field of topic modeling and applied in various fields such as software engineering, political science, medical and linguistic science, etc. There are various methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most popular in this field. Researchers have proposed various models based on the LDA in topic modeling. According to previous work, this paper will be very useful and valuable for introducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly articles (between 2003 to 2016) related to topic modeling based on LDA to discover the research development, current trends and intellectual structure of topic modeling. In addition, we summarize challenges and introduce famous tools and datasets in topic modeling based on LDA.
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Multimedia Tools and Applications (2019) 78:15169–15211
https://doi.org/10.1007/s11042-018-6894-4
Latent Dirichlet allocation (LDA) and topic modeling: models,
applications, a survey
Hamed Jelodar1·Yongli Wang1,2 ·Chi Yuan1·Xia Feng1·Xiahui Jiang1·Yanchao Li1·
Liang Zhao1
Received: 5 June 2018 / Revised: 28 October 2018 / Accepted: 13 November 2018 /
Published online: 28 November 2018
©Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract
Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers have
published many articles in the field of topic modeling and applied in various fields such as
software engineering, political science, medical and linguistic science, etc. There are var-
ious methods for topic modelling; Latent Dirichlet Allocation (LDA) is one of the most
popular in this field. Researchers have proposed various models based on the LDA in topic
modeling. According to previous work, this paper will be very useful and valuable for intro-
ducing LDA approaches in topic modeling. In this paper, we investigated highly scholarly
articles (between 2003 to 2016) related to topic modeling based on LDA to discover the
research development, current trends and intellectual structure of topic modeling. In addi-
tion, we summarize challenges and introduce famous tools and datasets in topic modeling
based on LDA.
Keywords Topic modeling ·Latent Dirichlet allocation ·Tag recommendation ·
Semantic web ·Gibbs sampling
Yongli Wang
YongliWang@njust.edu.cn
Hamed Jelodar
Jelodar@njust.edu.cn
Chi Yuan
yuanchi@njust.edu.cn
Xia Feng
779477284@qq.com
Xiahui Jiang
jxhchina@gmail.com
1School of Computer Science and Technology, Nanjing University of Science and Technology,
Nanjing 210094, China
2China Electronics Technology Cyber Security Co., Ltd, Chengdu, China
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Alternatively, probabilistic topic models are utilised in data mining and latent data discovery, as well as its most important implementation for discerning the relationship between data and text documents. Therefore, researchers have implemented the method to conduct aspect analyses [34,35]. For example, Latent Dirichlet Allocation (LDA) [36] is a popular unsupervised admixture or topic model, which specifically builds a set of topics based on an input collection of documents. ...
... For example, Latent Dirichlet Allocation (LDA) [36] is a popular unsupervised admixture or topic model, which specifically builds a set of topics based on an input collection of documents. As a parametric model, it incorporates the concept of Bag of Words (BoWs) for the articulation of topics; the standard LDA has been adopted by many researchers for aspect extraction [34,35,37,38]. Meanwhile, others have considered lexicon-based topic models [39][40][41], whereas works such as [42][43][44][45] have opted for distributed vector method and knowledge-based information as a supplementary approach for aspect analysis. ...
... Currently, the proposed topic models for aspect-analysis are mainly built on the premises of parametric model (i.e. LDA) as seen in different surveys such as [34,35,46], whereas a few others rely on its non-parametric counterpart (i.e. HDP) [47,48]. ...
SAR-HDP: Non-parametric Topic Model for Aspect categorisation based on online reviews
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Aspect categorisation and its utmost importance in the field of Aspect-based Sentiment Analysis (ABSA) has encouraged researchers to improve topic model performance for modelling the aspects into categories. In general, a majority of its current methods implement parametric models requiring a predetermined number of topics beforehand. However, this is not efficiently undertaken with unan-notated text data as they lack any class label. Therefore, the current work presented a novel non-parametric model drawing a number of topics based on the semantic association present between opinion-targets (i.e., aspects) and their respective expressed sentiments. The model incorporated the Semantic Association Rules (SAR) into the Hierarchical Dirichlet Process (HDP), named ('SAR-HDP'). The phrase-based (or aspect-based) Bayesian model (SAR-HDP) did not consider the word's sentence being drawn from a single topic due to the presence of multiple aspects in a single review, which belonged to a multiple-aspect topic (i.e., category). Beyond its consideration of the semantic information for aspect identification , the proposed model further upheld the semantic information discerned between the drawn topics and aspects identified to maintain topic consistency. Empirical investigation showed that the approach positioned successfully outper-formed standard parametric models and nonparametric models in terms of aspect categorisation when subjected to restaurant and hotel reviews sourced from Amazon and TripAdvisor.
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... Latent Dirichlet Allocation (LDA) merupakan model probabilistik generatif dari korpus, dimana dokumen direpresentasikan sebagai kumpulan topik laten dengan distribusi kata-kata yang akan membedakan setiap topik [9]. LDA berasumsikan bahwa setiap dokumen terdiri dari campuran topik yang berbeda yang masing-masing dapat dijelaskan melalui distribusi probabilitas. ...
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... It identifies hidden topics in a collection of documents by analysing word frequency and distribution. LDA assumes each document is a mixture of topics, and each topic is represented by a set of words with varying probabilities (Jelodar et al., 2018). Researchers can specify the number of topics (k) to generate and the number of keywords (n) associated with each topic. ...
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... LDA [23], a generative probabilistic model, was used to uncover latent themes in the textual data. LDA assumes that each post is a mixture of topics, and each topic is a distribution over words. ...
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... Topic modeling is widely acknowledged as a method for discovering the latent themes that characterize a collection of documents (for recent reviews with emphasis on applications, see Churchill and Singh 2021;Jelodar, Wang, Yuan, Feng, Jiang, Li and Zhao 2019). Mathematically, a topic is defined as a multinomial distribution over a collection of words that constitute a vocabulary. ...
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Preprint
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... Topic identification helps determine content suitability, while latent themes evaluate inter-document correlations [4]. Latent Dirichlet Allocation (LDA) [5,6], a probabilistic framework for document collections, has proven effective for multi-document summarization [7][8][9][10][11]. ...
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... We then imported the.bib file into the R environment to automate and facilitate the analysis. Topic modelling (see Jelodar et al., 2019) was employed to identify the top terms in the literature. A number of preprocessing steps in R were carried out to clean and normalise the textual data. ...
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