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# Recognition of emotions, valence and arousal in large-scale multi-domain text reviews

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In this article, we present a novel multidomain dataset of Polish text reviews. The data were annotated as part of a large study involving over 20,000 participants. A total of 7,000 texts were described with metadata, each text received about 25 annotations concerning polarity, arousal and eight basic emotions, marked on a multilevel scale. We present a preliminary approach to data labelling based on the distribution of manual annotations and to the classification of labelled data using logistic regression and bi-directional long short-term memory recurrent neural networks.
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Recognition of emotions, valence and arousal
in large-scale multi-domain text reviews
Jan Koco´
n, Arkadiusz Janz, Piotr Miłkowski, Monika Riegel, Małgorzata Wierzba,
Artur Marchewka, Agnieszka Czoska‡, Damian Grimling ‡,
Barbara Konat‡¡, Konrad Juszczyk‡¡, Katarzyna Klessa¡, Maciej Piasecki
Wroclaw University of Science and Technology
Wybrze˙
ze Wyspia´
nskiego 27, 50-370 Wrocław, Poland
Laboratory of Brain Imaging, Nencki Institute of Experimental Biology of Polish Academy of Sciences
Ludwika Pasteura 3, 02-093 Warszawa
{a.marchewka, m.riegel, m.wierzba}@nencki.gov.pl
‡W3A.PL Sp. z o.o.
Pi ˛atkowska 110A/1, 60-649 Pozna´
n, Polska
¡Adam Mickiewicz University, Faculty of Modern Languages and Literatures
Niepodległo´
sci 4, 61-874 Pozna´
n
{klessa}@amu.edu.pl
Abstract
In this article, we present a novel multidomain dataset of Polish text reviews. The data were annotated as part of a large study involving
over 20,000 participants. A total of 7,000 texts were described with metadata, each text received about 25 annotations concerning
polarity, arousal and eight basic emotions, marked on a multilevel scale. We present a preliminary approach to data labelling based on
the distribution of manual annotations and to the classiﬁcation of labelled data using logistic regression and bi-directional long short-term
memory recurrent neural networks.
1. Introduction
Emotions are a crucial part of natural human commu-
nication, conveyed by both what we say and how we say it.
In this study, we focus on emotions attributed by Polish na-
tive speakers to written Polish texts. The results presented
in this paper combine machine learning with an empirical
approach to language and emotions expressed verbally.
Introduction of machine learning (ML) to the area of
text mining resulted in the rapid growth of the ﬁeld in re-
cent years. However, an automatic emotion recognition
with Machine Learning remains a challenging task due to
the scarcity of high quality and large scale data sources.
Numerous approaches were attempted to annotate words
concerning their polarity and emotions for various lan-
guages (Riegel et al., 2015). Such datasets, however, are
limited in size, typically consisting of several thousands of
words, while lexicons are known to be much bigger.1The
size of the known and annotated affective word lists con-
strains their usage in natural language processing.
In emotion research, words are usually characterised
according to two dominant theoretical approaches to the
nature of emotion: dimensional account and categorical
account. According to the ﬁrst account proposed in (Rus-
sell and Mehrabian, 1977), each emotion state can be
1The largest dictionary of English, Oxford English Dictio-
nary, for example, contains around 600,000 words in its online
represented by its location in a multidimensional space,
with valence or polarity (negativity/positivity) and arousal
(low/high) explaining most of the observed variance. In the
competing account, several basic or elementary emotion
states are distinguished, with more complex, subtle emo-
tion states emerging as their combination. To categorise
emotions, semantic concepts drawn from natural language
are used, as corresponding to particular behavioural or
physiological response patterns. The concept of basic
emotions itself has been interpreted in various ways, and
thus different theories posit different numbers of categories
of emotion, with (Ekman, 1992) and (Plutchik, 1982) gain-
ing most recognition in the scientiﬁc community.
On the other hand, the most popular approach in natural
language processing, but also in applied usages of emotion
annotation is sentiment analysis which takes into account
only polarity (negativity/positivity). It is understandable
since the emotion annotation of textual data faces difﬁcul-
ties in the two conventional approaches to annotation. In
the ﬁrst approach, a small number (usually 2 to 5) trained
annotators are engaged and because of the differences be-
tween individual opinions, enhanced by multiple choice
possibilities (most commonly 6 or 8 emotions), may lead
to poor results of inter-annotator agreement (Hripcsak and
Rothschild, 2005). The other approach, based on crowd
annotations on platforms such as Amazon Turk (Paolacci
and Chandler, 2014) leads to a similar problem: the inter-
labeller variability of annotations is high because such plat-
forms are open to users of different nationalities while only
the native speakers of a given language can distinguish
subtleties of emotional connotations.
In this study, we applied an approach that proved useful
in previous experiments (Riegel et al., 2015). Thus, our an-
notation schema follows the account of Russel and Mehra-
bian, as well as those proposed by Ekman or Plutchik. Fi-
nally, by combining simple annotation schema with crowd
annotation, we were able to effectively acquire a large
amount of data, while at the same time preserving the high
quality of the data. Sentiment analysis enhanced with eight
basic emotions leads to new possibilities of studying peo-
ple’s attitudes towards brands, products and their features,
political views, movie or music choices or ﬁnancial deci-
sions, including stock exchange activity. Moreover, com-
paring the results of meaning and text ranking leads to
a better understanding of text processing, especially con-
structing the emotional meaning of texts by readers.
2. Data annotation
To create Sentimenti database, a total of over 20,000
unique respondents (with approximately equal number of
male and female participants) was sampled from Polish
population (sex, age, native language, place of residence,
education level, marital status, employment status, politi-
cal beliefs and income were controlled, among other fac-
tors). To collect the data, a combined approach of different
methodologies was used, namely: Computer Assisted Per-
sonal Interview (CAPI) and Computer Assisted Web Inter-
view (CAWI).
The annotation schema was based on procedures most
widely used in previous studies aiming to create the ﬁrst
datasets of Polish words annotated in terms of emotion
(NAWL, (Riegel et al., 2015); NAWL BE, (Wierzba et al.,
2015); plWordNet-emo (Za´
sko-Zieli´
nska et al., 2015; Janz
et al., 2017)). Thus, we collected extensive annotations of
valence (polarity), arousal, as well as eight emotion cate-
gories: joy, sadness, trust, disgust, fear, anger, surprise and
anticipation.
The total number of over 30,000 word meanings from
Polish WordNet (Piasecki et al., 2009) was annotated, with
each meaning ranked at least 50 times on each scale. The
selection of word meanings was based on the results of
the plWordNet-emo (Za´
sko-Zieli´
nska et al., 2015) project,
in which linguists annotated over 87K lexical units with
over 178K annotations containing information about emo-
tions, valence (polarity) and valuations (statistics from
May 2019). At the time when the selection was made (July
2017) 84K annotations were covering 54K word meanings
and 41K synsets. We observed that 27% of all annotations
(23K) were not neutral. The number of synsets having lex-
ical units with polarity different than neutral was 9K. We
have adopted the following assumptions for the selection
procedure:
word meanings that we know are not neutral are more
important,
polarity sign of the synset is the polarity sign of word
meanings within the synset (valid in 96% of cases),
• the maximum number of selected word meanings
from the same synset is 3,
the degree of synsets (treated as nodes in plWordNet
graph) which are sources of selected word meanings
should be in range [3,6].
Word meanings were presented to respondents as colloca-
tions manually prepared by linguists.
Moreover, in a follow-up study, a total number of over
7,000 texts (short phrases or paragraphs of text) were an-
notated in the same way, with each text assessed at least
25 times on each scale. Before attempting the assess-
ment task, subjects were instructed to rank word mean-
ings rather than words, as well as encouraged to indicate
their immediate, spontaneous reactions. Participants had
unlimited time to complete the task, and they were able to
quit the assessment session at any time and resume their
work later on. The ﬁnal collection of texts for emotive an-
notation was acquired from Web reviews of two distinct
domains: medicine2(2000 reviews) and hotels3(2000 re-
views). Due to the scarcity of neutral reviews in these data
sources, we decided to acquire yet another sample from po-
tentially neutral Web sources being thematically consistent
with selected domains, i.e. medical information sources
4(500 paragraphs) and hotel industry news 5(500 para-
graphs). The phrases for annotation were extracted using
lexico-semantic-syntactic patterns (LSS) manually created
by linguists to capture one of the four effects affecting sen-
timent: increase, decrease, transition, drift. Most of these
phrases belong to previously mentioned thematic domains.
The source for the remaining phrases were Polish WordNet
glosses and usage examples (Piasecki et al., 2009).
3. Data transformation
We decided to carry out the recognition of speciﬁc di-
mensions as a classiﬁcation task. Eight basic emotions
were annotated by respondents on a scale of integers from
range [0,4] and the same scale was also used for arousal di-
mension. For valence dimension, a scale of integers from
range [3,3] was proposed to obtain a more clear gra-
dation of effect size. We divided the valence scores into
two groups: positive (valence_p) and negative (valence_n).
This division results from the fact that there were texts
that received scores from both polarities. We wanted to
keep that distribution (see Algorithm 1). For the rest of
dimensions, we assigned the average value of all scores
(normalised to the range [0,1]) to the text.
3.1. Scores distribution
As a part of this study, a collection of 7004 texts was
annotated. To investigate the underlying empirical distri-
bution of emotive scores we analysed our data concerning
each dimension separately. We performed two statistical
tests to verify the multimodality of scores distribution in
our sample for each dimension. The main purpose of this
2www.znanylekarz.pl
4naukawpolsce.pap.pl/zdrowie
5hotelarstwo.net,www.e-hotelarstwo.com
Algorithm 1 Estimating the average value of positive and
negative valence for a single review.
Require: V: list of all valence scores;
m= 3: the maximum absolute value of polarity;
Ensure: Pair (p, n)where pis average positive valence,
and nis average negative valence;
1: (p, n) = (0,0)
2: for vVdo
3: if v < 0then n=n+|v|else p=p+v
return p÷(|V| · m), n ÷(|V| · m)
analysis was to identify if there exists a speciﬁc decision
boundary splitting our data into distinct clusters, to sep-
arate the examples sharing the same property (e.g. posi-
erty (e.g. non-positive texts). The ﬁrst test was Harti-
gans’ dip test. It uses the maximum difference for all av-
eraged scores, between the empirical distribution function,
and also the unimodal distribution function that minimises
the maximum difference (Hartigan et al., 1985). There
are the unimodal null hypothesis and a multimodal alterna-
tive. The second one is Silverman’s mode estimation test
which uses kernel density estimation methods to examine
the number of modes in a sample (Silverman, 1981). If the
null hypothesis of unimodality (k= 1) was rejected, we
also tested if there are two modes (k= 2) or more (Neville
and Brownstein, 2018). We used locmodes R package to
apply statistical testing (Ameijeiras-Alonso et al., 2016)
with Hartigans’ and Silverman’s tests on our annotation
data. For all dimensions we could not reject the null hy-
pothesis of bimodality and only in 2 cases (arousal, dis-
gust) we could reject the null hypothesis of unimodality by
the result of both tests (see Table 1).
Dimension SI_mod1 SI_mod2 HH
valence_n 0.000 0.812 0.000
valence_p 0.000 0.460 0.000
arousal 0.340 0.606 0.118
joy 0.000 0.842 0.000
fear 0.892 0.674 0.032
disgust 0.784 0.500 0.178
surprise 0.288 0.360 0.000
anticipation 0.522 0.321 0.000
trust 0.034 0.736 0.226
anger 0.000 0.630 0.000
Table 1: p–values for Silverman’s test with k= 1
(SI_mod1), k= 2 (SI_mod2) and Hartigans’ dip test (HH).
The distributions of averaged scores for all texts are
presented in Figure 1. We decided to partition all scores for
each dimension into two clusters using k-means cluster-
ing (Hartigan and Wong, 1979). Clusters are represented
in Figure 1 with different colours. We assign a label (cor-
responding to the dimension) if the score for the dimension
is higher than the threshold determined by k-means. Each
review may be described with multiple labels.
Figure 1: Distribution of avg. scores for all dimensions.
4. Experiments
In our experimental part, we decided to use a popular
baseline model based on fastText algorithm (Bojanowski
et al., 2017; Joulin et al., 2017) as a reference method for
the evaluation. FastText’s supervised models were used in
many NLP tasks, especially in the area of sentiment analy-
sis, e.g. for hate speech detection (Badjatiya et al., 2017),
emotion and sarcasm recognition (Felbo et al., 2017) or
aspect-based sentiment analysis in social media (Wojatzki
et al., 2017). The unsupervised fastText models were
also used to prepare word embeddings of Polish (see Sec-
tion 4.1.). In our experiments, we used supervised fastText
models as a simple multi-label text classiﬁer for sentiment
and emotion recognition. We used one-versus-all cross-
entropy loss and 250 training epochs, with KGR10 pre-
trained word vectors (Koco´
n and Gawor, 2019) (described
in Section 4.1.) for all evaluation cases.
In recent years deep neural networks have begun
to dominate natural language processing (NLP) ﬁeld.
The most popular solutions incorporate bidirectional long
short-term memory neural networks (henceforth BiL-
STM). BiLSTM-based approaches were mainly applied in
the information extraction area, e.g. in the task of proper
names recognition, where the models are often combined
with conditional random ﬁelds (CRF) to impose additional
constraints on sequences of tags as presented in (Habibi
et al., 2017).
LSTM networks have proved to be very effective in
sentiment analysis, especially for the task of polarity de-
tection (Wang et al., 2016; Baziotis et al., 2017; Ma
et al., 2018). In this study, we decided to adopt the
multi-labelled BiLSTM networks and expand our research
to the more challenging task of emotion detection. As
an input for BiLSTM networks we used pre-trained fast-
Text embeddings trained on KGR10 corpus (Koco´
n and
Gawor, 2019). The parameters used for training pro-
cedure were as follows: MAX_WORDS=128 (94% of re-
views have 128 words or less), HIDDEN_UNITS=1024,
LEARNING_RATE=0.001,BATCH_SIZE=128.
4.1. Word embeddings
The most popular text representations in recent ma-
chine learning solutions are based on word embeddings.
Dense vector space representations follow the distribu-
tional hypothesis that the words with similar meaning tend
to appear in similar contexts. Word embeddings capture
the similarity between words and are often used as an in-
put for the ﬁrst layer of deep learning models. Contin-
uous Bag-of-Words (CBOW) and Skip-gram (SG) models
are the most common methods proposed to generate dis-
tributed representations of words embedded in a continu-
ous vector space (Mikolov et al., 2013).
With the progress of machine learning methods, it is
possible to train such models on larger data sets, and these
models often outperform the simple ones. It is possible
to use a set of text documents containing even billions of
words as training data. Both architectures (CBOW and SG)
describe how the neural network learns the vector repre-
sentations for each word. In CBOW architecture the task
is predicting the word given its context, and in SG the task
is predicting the context given the word.
Numerous methods have been developed to prepare
vector space representations of words, phrases, sentences
or even full texts. The quality of vector space models
depends on the quality and the size of the training cor-
pora used to prepare the embeddings. Hence, there is a
strong need for proper evaluation metrics, both intrinsic
and extrinsic (task-based evaluation), to evaluate the qual-
ity of vector space representations including word embed-
dings (Schnabel et al., 2015), (Piasecki et al., 2018). Pre-
trained word embeddings built on various corpora are al-
ready available for many languages, including the most
representative group of models built for English (Kutuzov
et al., 2017) language.
In (Koco´
n and Gawor, 2019) we introduced mul-
tiple variants of word embeddings for Polish built on
KGR10 corpora. We used the implementation of CBOW
and Skip-gram methods provided with fastText tool (Bo-
janowski et al., 2017). These models are available un-
der an open license in the CLARIN-PL project reposi-
tory6. With these embeddings, we obtained a favourable
results in two NLP tasks: recognition of temporal ex-
pressions (Koco´
n and Gawor, 2019) and recognition of
named entities (Marci´
nczuk et al., 2018). For this rea-
son, the same model of word embeddings was used
for this work, which is EC1 (Koco´
n and Gawor, 2019)
(kgr10.plain.skipgram.dim300.neg10.bin).
6https://clarin-pl.eu/dspace/handle/
11321/606
4.2. Evaluation procedure
We prepared three evaluation scenarios to test the per-
formance of fastText and BiLSTM baseline models. The
most straightforward scenario is a single domain setting
(SD) where the classiﬁer is trained and tested on the data
representing the same thematic domain. In a more realistic
scenario, the thematic domain of training data differs from
the application domain. This means that there may exist
a discrepancy between feature spaces of training and test-
ing data which leads to a signiﬁcant decrease of classiﬁer’s
performance in the application domain. To test the clas-
siﬁer’s ability to bridge the gap between source and target
domains we propose a second evaluation scenario called 1-
Domain-Out (DO). This scenario is closely related to the
focus on transferring the knowledge from labelled training
data to unlabelled testing data. The last evaluation scenario
is a multidomain setting where we merge all available la-
belled data representing different thematic domains into a
single training dataset (MD).
Single Domain, SD – train/dev/test sets are from the
same domain (3 settings, metric: F1-score).
1-Domain-Out, DO – train/dev sets are from two do-
mains, test set is from the third domain (3 settings,
metric: F1-score).
Mixed Domains, MD – train/dev/test sets are ran-
domly selected from all domains (1 setting, metrics:
precision, recall, F1-score, AUC_ROC).
We prepared seven evaluation settings with a different
domain-based split of the initial set of texts. The ﬁnal di-
vision is presented in Table 2.
Type Setting Train Dev Test SUM
SD
Hotels 2504 313 313 3130
Medicine 2352 293 293 2938
Other 750 93 93 936
DO
Hotels-Other 3660 406 - 4066
Hotels-Medicine 5462 606 - 6068
Medicine-Other 3487 387 - 3874
MD All 5604 700 700 7004
Table 2: The number of texts in the evaluation settings.
To tune our baseline methods we decided to use a dev
set. We calculated the optimal decision threshold for each
dimension using receiver operating characteristic (ROC)
curve, taking the threshold which produces the point on
ROC closest to (FPR,TPR) = (0,1).
5. Results
Table 3 shows the results for SD evaluation. There are
11 results for each of the 3 domains. BiLSTM classiﬁer
outperformed FastText in 27 out of 33 cases. Table 4 shows
the results for DO evaluation. Here BiLSTM classiﬁer pro-
vided better quality for 31 out of 33 cases. The last MD
evaluation results are in Table 5 (P, R, F1-score) and Fig-
ure 2 (ROC). BiLSTM outperformed FastText in 31 out of
36 cases (Table 5). ROC_AUC is the same for both clas-
siﬁers in 4 cases (2 of them are micro and macro-average
ROC). For the rest of the curves, BiLSTM outperformed
FastText in 7 out of 9 cases. The most interesting phe-
nomenon can be observed in Table 4 where the differences
are the greatest. This may indicate that the deep neural
network was able to capture domain-independent features
(pivots) which is an important ability for domain adaption
Figure 2: ROC curves for FastText and BiLSTM classi-
ﬁers.
6. Conclusions
In this preliminary study, we focused on basic neu-
ral language models to prepare and evaluate baseline ap-
proaches to recognise emotions, valence and arousal in
multi-domain textual reviews. Further plans include the
evaluation of hybrid approaches combining machine learn-
ing approaches and lexico-syntactic rules augmented with
semantic analysis of word meanings. We also plan to au-
tomatically expand the annotations of word meanings to
the rest of lexical units within plWordNet using the prop-
agation methods presented in (Koco´
n et al., 2018a; Koco´
n
et al., 2018b). We intend to test other promising methods
later, such as Google BERT (Devlin et al., 2018), Ope-
nAI GPT-2 (Radford et al., 2019) and domain dictionaries
construction methods utilising WordNet (Koco´
n and Mar-
ci´
nczuk, 2016).
Automatic emotion annotation has both scientiﬁc and
applied value. Modern business is interested in the opin-
ions, emotions and values associated with brands and prod-
ucts. Retailers and merchants collect vast amounts of cus-
tomer feedback and rumours both from in-store and posted
online. What is more, relation departments monitor the
impact of their campaigns and need to know whether it
was positive and touching for customers. In this context,
the results of monitoring opinions, reactions, and emotions
present great value, because they fuel decisions and be-
haviour (Tversky and Kahneman, 1989). However, most
of the existing solutions are still limited to manual annota-
tion and simpliﬁed methods of analysis.
The large database built in the Sentimenti project cov-
ers a wide range of Polish vocabulary and introduces an
extensive emotive annotation of word meanings in terms
of their polarity, basic emotions and affective arousal. The
results of such research can be used in several applications
– media monitoring, chatbots, stock prices forecasting,
types of content. In the last decades, the development of
Internet services gave us an unprecedented amount of data,
resulting in the big data revolution (Kitchin, 2014). This
also includes the textual data coming directly from social
media and other sources.
We also provide a preliminary overview of ML meth-
ods for automatic analysis of people’s opinions in terms
of expressed emotions and their attitudes. Since the par-
ticipants of our CAPI and CAWI studies represent a wide
cross-section of population we can adapt our methods to
speciﬁc target groups of people. This introduces the much
needed human aspect to artiﬁcial intelligence and machine
learning in natural language processing.
7. Data availability
Due to the commercial nature of the Sentimenti project,
it is planned to make 10% of the project data available
soon. The data will be published at www.sentimenti.pl.
We will consider making more data accessible in the fu-
ture.
Acknowledgements
Co-ﬁnanced by the Polish Ministry of Education and
Science, CLARIN-PL Project and by the National Cen-
tre for Research and Development, Poland, grant no
POIR.01.01.01-00-0472/16 – Sentimenti (http://w3a.
pl/projekty/).
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Setting
Classiﬁer
Valencep
Valencen
Arousal
Joy
Surprise
Anticipation
Trust
Anger
Fear
Disgust
1. Hotels FastText 90.53 88.43 66.67 89.08 62.63 77.91 83.41 86.04 88.33 65.81 81.86
BiLSTM 89.74 89.54 67.66 86.84 46.62 82.11 80.83 88.46 89.54 63.53 82.76
2. Medicine FastText 75.37 56.18 61.54 75.00 62.00 75.49 74.14 64.32 59.09 45.90 73.20
BiLSTM 82.18 82.40 65.31 84.15 64.38 80.31 82.47 86.33 85.23 83.04 74.04
3. Other FastText 66.67 66.67 62.34 62.86 48.57 51.52 45.28 77.27 48.15 45.28 46.51
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Table 3: F1-scores for Single Domain evaluation. (Train, Dev, Test) sets for settings are the same as in Table 2, rows 1-3.
Setting
Classiﬁer
Valencep
Valencen
Arousal
Joy
Surprise
Anticipation
Trust
Anger
Fear
Disgust
4. Hotels-Other
vs Medicine
FastText 61.44 72.79 63.08 61.73 59.03 58.10 65.54 75.27 71.97 71.33 63.20
BiLSTM 74.56 76.61 66.00 71.25 62.62 70.32 67.52 80.40 73.97 74.03 69.80
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vs Hotels
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Table 4: F1-scores for 1-Domain-Out evaluation. (Train/Dev, Test) sets (see Table 2) for these settings are: 4. (Hotels-
Other.Train/Dev, Medicine.Test), 5. (Hotels-Medicine.Train/Dev, Other.Test), 6. (Medicine-Other.Train/Dev, Hotels.Test).
Dim. FastText BiLSTM
P R F P R F
Valencep73.41 77.41 75.36 77.61 84.10 80.72
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Joy 70.61 81.14 75.51 77.51 84.65 80.92
Surprise 65.07 64.31 64.69 67.67 59.88 63.54
Anticip. 72.28 77.66 74.78 79.66 81.91 80.77
Trust 65.32 79.02 71.52 73.91 82.93 78.16
Sadness 81.73 82.55 82.14 83.88 85.57 84.72
Anger 80.92 78.52 79.70 82.03 89.63 85.66
Fear 69.20 77.78 73.24 68.84 81.20 74.51
Disgust 66.80 77.73 71.85 71.71 84.09 77.41
Avg. 71.69 77.48 74.38 75.57 80.87 78.02
Table 5: Precision, recall and F1-score for Mixed Domains
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... So far, Polish language corpora have been developed, which are the subject of research projects on sentiment analysis. For the Sentimenti project, in addition to annotating selected lexical units from the Polish wordnet (Słowosieć), emotional annotation was also applied to consumer reviews [32]. This corpus (PolEmo) is continuously used to develop methods for machine extraction of sentiment from texts [32], [33]. ...
... For the Sentimenti project, in addition to annotating selected lexical units from the Polish wordnet (Słowosieć), emotional annotation was also applied to consumer reviews [32]. This corpus (PolEmo) is continuously used to develop methods for machine extraction of sentiment from texts [32], [33]. The corpus established within the framework of the PolEval 2017 project [34], which contains the opinions of users of various types of products (such as perfume, clothes) with added negative, positive, and neutral annotations, is definitely noteworthy. ...
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... sets of word meanings representing the same concept). We used the following criteria for the selection process (Kocoń et al., 2019): (1) we chose non-neutral word meanings first; (2) the maximum number of selected word meanings belonging to one synset was 3; (3) the degree of the synset node containing a word meaning (number of relations to other synsets) in the plWordNet graph was in the range of 3-6. ...
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