<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE root>
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Russian Journal of Linguistics</journal-id><journal-title-group><journal-title xml:lang="en">Russian Journal of Linguistics</journal-title><trans-title-group xml:lang="ru"><trans-title>Russian Journal of Linguistics</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2687-0088</issn><issn publication-format="electronic">2686-8024</issn><publisher><publisher-name xml:lang="en">Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">39433</article-id><article-id pub-id-type="doi">10.22363/2687-0088-35624</article-id><article-id pub-id-type="edn">DFZGBB</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="zh"><subject>Articles</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">The difference in positivity of the Russian and English lexicon: The big data approach</article-title><trans-title-group xml:lang="ru"><trans-title>Различие позитивности в лексиконах русского и английского языков: подход, основанный на больших данных</trans-title></trans-title-group><trans-title-group xml:lang="zh"><trans-title/></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-4692-2564</contrib-id><name-alternatives><name xml:lang="en"><surname>Solovyev</surname><given-names>Valery D.</given-names></name><name xml:lang="ru"><surname>Соловьев</surname><given-names>Валерий Дмитриевич</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><bio xml:lang="en"><p>Doctor Habil. of Physical and Mathematical Sciences, Professor, Chief Researcher of “Text Analytics” Research Lab, Institute of Philology and Intercultural Communication of Kazan Federal University, Kazan, Russia. He is a member of the Presidium of the Interregional Association for Cognitive Research, author of four monographs and more than 60 publications on the computational linguistics.</p></bio><bio xml:lang="ru"><p>доктор физико-математических наук, профессор, главный научный сотрудник НИЛ «Текстовая аналитика» Института филологии и межкультурной коммуникации Казанского федерального университета, Казань, Россия. Член президиума Межрегиональной ассоциации когнитивных исследований. Автор четырех монографий и более 60 публикаций по компьютерной лингвистике.</p></bio><email>maki.solovyev@mail.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-2670-6795</contrib-id><name-alternatives><name xml:lang="en"><surname>Ivleva</surname><given-names>Anna I.</given-names></name><name xml:lang="ru"><surname>Ивлева</surname><given-names>Анна Игоревна</given-names></name><name xml:lang="zh"><surname></surname><given-names></given-names></name></name-alternatives><bio xml:lang="en"><p>Ph.D. in Engineering Sciences and Senior Researcher of “Linguistics and AI” Research Lab, Institute of Philology and Intercultural Communication of Kazan Federal University, Kazan, Russia. The main areas of her research interests are quantitative linguistics, natural language processing and translation studies.</p></bio><bio xml:lang="ru"><p>кандидат технических наук, старший научный сотрудник НИЛ «Лингвистика и искусственный интеллект» Института филологии и межкультурной коммуникации Казанского федерального университета, Казань, Россия. Основные сферы научных интересов: квантитативная лингвистика, обработка естественного языка, теория перевода.</p></bio><email>ivleva.anna.igorevna@yandex.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Kazan Federal University</institution></aff><aff><institution xml:lang="ru">Казанский федеральный университет</institution></aff><aff><institution xml:lang="zh"></institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2024-06-07" publication-format="electronic"><day>07</day><month>06</month><year>2024</year></pub-date><volume>28</volume><issue>2</issue><issue-title xml:lang="en">VOL 28, NO2 (2024)</issue-title><issue-title xml:lang="ru">ТОМ 28, №2 (2024)</issue-title><fpage>266</fpage><lpage>293</lpage><history><date date-type="received" iso-8601-date="2024-06-07"><day>07</day><month>06</month><year>2024</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2024, Solovyev V.D., Ivleva A.I.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2024, Соловьев В.Д., Ивлева А.И.</copyright-statement><copyright-statement xml:lang="zh">Copyright ©; 2024, Solovyev V., Ivleva A.</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="en">Solovyev V.D., Ivleva A.I.</copyright-holder><copyright-holder xml:lang="ru">Соловьев В.Д., Ивлева А.И.</copyright-holder><copyright-holder xml:lang="zh">Solovyev V., Ivleva A.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">https://creativecommons.org/licenses/by-nc/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/linguistics/article/view/39433">https://journals.rudn.ru/linguistics/article/view/39433</self-uri><abstract xml:lang="en"><p style="text-align: justify;">Psychological cross-cultural studies have long noted differences in the degree of cognition positivity, or optimism, in various cultures. Herewith, the question whether the difference shows up at the level of the language lexicon remains unexplored. Linguistic positivity bias has been confirmed for a number of languages. The point of it is that most words have a positive connotation in the language. This begs the question: is linguistic positivity bias the same for different languages or not? In a sense, the issue is similar to the hypothesis of linguistic relativity suggesting the language impact on the human cognitive system. The problem has been researched only in one work (Dodds et al. 2015), where data on the positivity bias values are given for different languages and the comparison for each pair of languages is based on merely one pair of dictionaries. In the present study, we radically increase the computational baseline by comparing four English and five Russian dictionaries. We carry out the comparative study both at the level of vocabularies and at the level of texts of different genres. A new, previously untapped idea is to compare positivity ratings of translated texts. Also, English and Russian sentiment dictionaries are compared based on the scores of translation-stable words. The results suggest that the Russian language is somewhat slightly more positive than English at the level of vocabulary.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">В психологических кросс-культурных исследованиях давно замечены различия в степени позитивности мышления, или оптимизма, в различных культурах. Напрашивается вопрос, является ли преимущественная лингвистическая позитивность (linguistic positivity bias) одинаковой для разных языков или нет. В определенном смысле этот вопрос схож с гипотезой лингвистической относительности, касающейся влияния языка на когнитивную систему человека. Эта проблема рассматривалась только в одной работе (Dodds et al. 2015), в которой представлены данные о разной величине преимущественной лингвистической позитивности для разных языков и где сравнение для двух языков проводилось с использованием всего одной пары словарей. В настоящем исследовании мы существенно увеличиваем вычислительную базу, сравнивая английский и русский языки на основе 4 английских и 5 русских словарей. Сравнение проводится как на уровне лексикона языков, так и на уровне текстов разных жанров. Новой, ранее не использовавшейся идеей, является сопоставление рейтингов позитивности переводных текстов. Также словари английского и русского языков сравниваются по значениям рейтингов слов, устойчивых к переводу ( translation-stable words ). Результаты позволяют предположить, что на уровне лексикона русский язык несколько более позитивен, чем английский.</p></trans-abstract><trans-abstract xml:lang="zh"/><kwd-group xml:lang="en"><kwd>sentiment</kwd><kwd>dictionaries</kwd><kwd>the Pollyanna principle</kwd><kwd>linguistic relativity</kwd><kwd>translated texts</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>тональность</kwd><kwd>словари</kwd><kwd>принцип Полианны</kwd><kwd>лингвистическая относительность</kwd><kwd>переводные тексты</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Madhusudhan, Aithal &amp; Tan Chenhao. 2021. On positivity bias in negative reviews. https://arxiv.org/pdf/2106.12056.pdf</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Bochkarev, Vladimir, Valery Solovyev, Timofei Nestik &amp; Anna Shevlyakova. 2023 Variations in average word valence of Russian books in response to social change over a century. Proceedings of the Artificial Intelligence and Natural Language Conference. Zap. Nauchn. Sem. POMI 529. 24-42.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Bradley, Margaret M. &amp; Peter. J. Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Boucher, Jerry &amp; Charles E. Osgood. 1969. The Pollyanna hypothesis. Journal of Verbal Learning and Verbal Behavior 8. 1-8. https://doi.org/10.1016/S0022-5371(69)80002-2</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Dodds, Peter Sheridan, Eric M. Clark, Suma Desu, Morgan R. Frank, Andrew J. Reagan, Jake Rylnd Williams, Lewis Mitchell, Kameron Decker Harris, Isabel M. Kloumann, James P. Bagrow, Karine Megerdoomian, Matthew T. McMahon, Brian F. Tivnan &amp; Christopher M. Danforth. 2015. Human language reveals a universal positivity bias. Proceedings of the National Academy of Sciences 112 (8). 2389-2394. https://doi.org/10.1073/pnas.1411678112</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Dodds, Peter Sheridan &amp; Christopher M. Danforth. 2010. Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies 11. 444-456. https://doi.org/10.48550/arXiv.1703.09774</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Dodds, Peter Sheridan, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss &amp; Christopher M. Danforth. 2011. Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLoS ONE 6 (12). e26752. https://doi.org/10.1371/journal.pone.0026752</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Folk, Dunigan &amp; Elizabeth Dunn. 2023. How can people become happier? A systematic review of preregistered experiments. Annual Review of Psychology 75.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Frank, Morgan R., Lewis Mitchell, Peter Sheridan Dodds &amp; Christopher M. Danforth. 2013. Happiness and the patterns of life: A study of geolocated tweets. Scientific Reports 3. 2625. https://doi.org/10.1038/srep02625</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Gallagher, Matthew W., Shane J. Lopez &amp; Sarah D. Pressman. 2013. Optimism is universal: Exploring the presence and benefits of optimism in a representative sample of the world. Journal of Personality 81 (5). 429-440. https://doi.org/10.1111/jopy.12026</mixed-citation></ref><ref id="B11"><label>11.</label><mixed-citation>Gower, Tricia, Kimberly S. Chiew, David Rosenfield &amp; Holly J. Bowen. 2023. Positive biases and psychological functioning during the coronavirus disease 2019 pandemic. Cognition and Emotion 37. 1-9. https://doi.org/10.1080/02699931.2023.2221022</mixed-citation></ref><ref id="B12"><label>12.</label><mixed-citation>Hills, Thomas T., Eugenio Proto, Daniel Sgroi &amp; Chanuki Illushka Seresinhe. 2019. Historical analysis of national subjective wellbeing using millions of digitized books. Nature Human Behaviour 3 (12). 1271-1275. https://doi.org/10.1038/s41562-019-0750-z</mixed-citation></ref><ref id="B13"><label>13.</label><mixed-citation>Iliev, Rumen, Joe Hoover, Morteza Dehghani &amp; Robert Axelrod. 2016. Linguistic positivity in historical texts reflects dynamic environmental and psychological factors. Proceedings of the National Academy of Sciences 113 (49). E7871-E7879. https://doi.org/10.1073/pnas.1612058113</mixed-citation></ref><ref id="B14"><label>14.</label><mixed-citation>Jackson, Joshua Conrad, Joseph Watts, Teague R. Henry, Johann. M. List, Robert Forkel, Peter Mucha, Simon J. Greenhill, Russell D. Gray &amp; Kristen A. Lindquist. 2019. Emotion semantics show both cultural variation and universal structure. Science 366 (6472). 1517-1522. https://doi.org/10.1126/science.aaw8160</mixed-citation></ref><ref id="B15"><label>15.</label><mixed-citation>Jacobs, Arthur M., Berenike Herrmann, Gerhard Lauer, Jana Lüdtke &amp; S. Schroeder. 2020. Sentiment analysis of children and youth literature: Is there a Pollyanna effect? Frontiers in Psychology 11. https://doi.org/10.3389/fpsyg.2020.574746</mixed-citation></ref><ref id="B16"><label>16.</label><mixed-citation>Jaidka, Kokil. 2022. Cross-platform-and subgroup-differences in the well-being effects of Twitter, Instagram, and Facebook in the United States. Scientific Reports 12 (1). 3271. https://doi.org/10.1038/s41598-022-07219-y</mixed-citation></ref><ref id="B17"><label>17.</label><mixed-citation>Ji, Li-Jun, Thomas. I. Vaughan-Johnston, Zhiyong Zhang, Jill A. Jacobson, Ning Zhang &amp; Xiaoye Huang. 2021. Contextual and cultural differences in positive thinking. Journal of Cross-Cultural Psychology 52 (5). 449-467. https://doi.org/10.1177/00220221211020442</mixed-citation></ref><ref id="B18"><label>18.</label><mixed-citation>Kassinove, Howard &amp; Denis G. Sukhodolsky. 1995. Optimism, pessimism and worry in Russian and American children and adolescents. Journal of Social Behavior &amp; Personality 10 (1). 157-168.</mixed-citation></ref><ref id="B19"><label>19.</label><mixed-citation>Kay, Paul &amp; Chad K. McDaniel. 1978. The linguistic significance of meanings of basic color terms. Language 54 (3). 610-646. https://doi.org/10.2307/412789</mixed-citation></ref><ref id="B20"><label>20.</label><mixed-citation>Kirchner-Häusler, Alexander, Michael Boiger, Yukiko Uchida, Yoko Higuchi, A. Uchida &amp; Batja Mesquita. 2022. Relatively happy: The role of the positive-to-negative affect ratio in Japanese and Belgian couples. Journal of Cross-Cultural Psychology 53 (1). 66-86. https://doi.org/10.3389/fpsyg.2020.01048</mixed-citation></ref><ref id="B21"><label>21.</label><mixed-citation>Kloumann, Isabel M., Christopher M. Danforth, Kameron Decker Harris, Catherine A. Bliss &amp; Peter Sheridan Dodds. 2012. Positivity of the English Language. PLoS ONE 7 (1). e29484. https://doi.org/10.48550/arXiv.1108.5192</mixed-citation></ref><ref id="B22"><label>22.</label><mixed-citation>Koltsova, Olesya Yu., Svetlana V. Alexeeva &amp; Sergey N. Kolcov. 2016. An opinion word lexicon and a training dataset for Russian sentiment analysis of social media. Computational Linguistics and Intellectual Technologies - Proceedings of the International Conference “Dialog” 277-287.</mixed-citation></ref><ref id="B23"><label>23.</label><mixed-citation>Kotel’nikov, Evgeniy V., Elena V. Razova, Anastasiya V. Kotelnikova &amp; Sergey V. Vychegzhanin. 2020. Modern sentiment lexicons for opinion mining in English and Russian (analytical survey). Informacionnye Processy i Sistemy 12. 16-33.</mixed-citation></ref><ref id="B24"><label>24.</label><mixed-citation>Kulagin, Denis I. 2021. Publicly available sentiment dictionary for the Russian language KartaSlovSent. Computational Linguistics and Intellectual Technologies - Proceedings of the International Conference “Dialog” 20. 1106-1119.</mixed-citation></ref><ref id="B25"><label>25.</label><mixed-citation>Kušen, Ema, Mark Strembeck &amp; Mauro Conti. 2019. Emotional valence shifts and user behavior on Twitter, Facebook, and YouTube. Influence and Behavior Analysis in Social Networks and Social Media. 63-83. https://doi.org/10.1007/978-3-030-02592-2_4</mixed-citation></ref><ref id="B26"><label>26.</label><mixed-citation>Larina, Tatiana &amp; Douglas Mark Ponton. 2022. I wanted to honour your journal, and you spat in my face: emotive (im) politeness and face in the English and Russian blind peer review. Journal of Politeness Research 18 (1). 201-226.</mixed-citation></ref><ref id="B27"><label>27.</label><mixed-citation>Liu, Bing. 2012. Sentiment Analysis and Opinion Mining. Springer.</mixed-citation></ref><ref id="B28"><label>28.</label><mixed-citation>McKee, Gerard T., David D. Malvern &amp; Brian James Richards. 2000. Measuring vocabulary diversity using dedicated software. Literary and Linguistic Computing 15 (3). 323-337. https://doi.org/10.1093/llc/15.3.323</mixed-citation></ref><ref id="B29"><label>29.</label><mixed-citation>Mitchell, Lewis, Kameron Decker Harris, Morgan R. Frank, Peter Sheridan Dodds &amp; Christopher M. Danforth. 2013. The geography of happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE 8 (5). e64417. https://doi.org/10.48550/arXiv.1302.3299</mixed-citation></ref><ref id="B30"><label>30.</label><mixed-citation>Mohammad, Saif M. 2018. Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 174-184. https://doi.org/10.18653/v1/P18-1017</mixed-citation></ref><ref id="B31"><label>31.</label><mixed-citation>Osgood, Charles E. 1952. The nature and measurement of meaning. Psychological Bulletin 49. 197-237.</mixed-citation></ref><ref id="B32"><label>32.</label><mixed-citation>Pang, Bo &amp; Lillian Lee. 2008. Opinion mining and sentiment analysis (English). Foundations and Trends in Information Retrieval 2. 1-135. https://doi.org/10.1561/1500000011</mixed-citation></ref><ref id="B33"><label>33.</label><mixed-citation>Panou, Despoina. 2013. Equivalence in translation theories: A critical evaluation. Theory and Practice in Language Studies 3 (1). 1-6. https://doi.org/10.4304/tpls.3.1.1-6</mixed-citation></ref><ref id="B34"><label>34.</label><mixed-citation>Reagan, Andrew J., Christopher M. Danforth, Brian F. Tivnan, Jake Ryland Williams &amp; Peter Sheridan Dodds. 2017. Sentiment analysis methods for understanding large-scale texts: A case for using continuum-scored words and word shift graphs. EPJ Data Science 6. 1-21. https://doi.org/10.1140/epjds/s13688-017-0121-9</mixed-citation></ref><ref id="B35"><label>35.</label><mixed-citation>Solnyshkina, Marina I., Valery D. Solovyev, Elzara V. Gafiyatova &amp; Ekaterina V. Martynova. 2023. Text complexity as interdisciplinary problem. Voprosy Kognitivnoy Lingvistiki 1. 18-39. https://doi.org/10.20916/1812-3228-2022-1-18-39</mixed-citation></ref><ref id="B36"><label>36.</label><mixed-citation>Solovyev, Valery, Musa Islamov &amp; Venera Bayrasheva. 2022. Dictionary with the evaluation of positivity/negativity degree of the Russian words. In S. R. Mahadeva Prasanna, Alexey Karpov, K. Samudra Vijaya &amp; Shyam S. Agrawal (eds.), Speech and computer. SPECOM 2022. Lecture notes in computer science, 13721, 651-664. Springer.</mixed-citation></ref><ref id="B37"><label>37.</label><mixed-citation>Solovyev, Valery, Vladimir Ivanov. 2014. Dictionary-based problem phrase extraction from user reviews. In Petr Sojka, Alex Horák, Ivan Kopeček &amp; Karel Pala (eds.), Text, speech and dialogue. TSD 2014. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in Bioinformatics), LNAI, 8655, 225-232. Springer.</mixed-citation></ref><ref id="B38"><label>38.</label><mixed-citation>Solovyev, Valery D., Marina I. Solnyshkina &amp; Danielle S. McNamara. 2022. Computational linguistics and discourse complexology: Paradigms and research methods. Russian Journal of Linguistics 26 (2). 275-316. https://doi.org/10.22363/2687-0088-31326</mixed-citation></ref><ref id="B39"><label>39.</label><mixed-citation>Tausczik, Yla R. &amp; James W. Pennebaker. 2014. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29 (1). 24-54. https://doi.org/10.1177/0261927X09351676</mixed-citation></ref><ref id="B40"><label>40.</label><mixed-citation>Tetior, Alexander N. 2015. The emotional sphere of a person: The predominance of negative emotions. Eurasian Union of Scientists 2 (11). 78-81.</mixed-citation></ref><ref id="B41"><label>41.</label><mixed-citation>Warriner, Amy Beth, Victor Kuperman &amp; Marc Brysbaert. 2013. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods 45. 1191-1207. https://doi.org/10.3758/s13428-012-0314-x</mixed-citation></ref><ref id="B42"><label>42.</label><mixed-citation>Warriner, Amy Beth &amp; Victor Kuperman. 2015. Affective biases in English are bi-dimensional. Cognition and Emotion 29 (7). 1147-1167. https://doi.org/10.1080/02699931.2014.968098</mixed-citation></ref><ref id="B43"><label>43.</label><mixed-citation>Whorf, Benjamin Lee. 2012. Language, Thought, and Reality: Selected Writings of Benjamin Lee Whorf. In John B. Carroll, Stephen C. Levinson &amp; Penny Lee (eds.). The MIT Press.</mixed-citation></ref><ref id="B44"><label>44.</label><mixed-citation>Wierzbicka, Anna. 1992. The Russian language. Semantics, Culture and Cognition: Universal Human Concepts in Culture-specific Cofigurations. 395-441. New York: Oxford University Press.</mixed-citation></ref><ref id="B45"><label>45.</label><mixed-citation>Yoonjung, Choi &amp; Wiebe Janyce. 2014. +/-EffectWordNet: Sense-level lexicon acquisition for opinion inference. Proc. of EMNLP. 1181-1191. https://doi.org/10.3115/v1/D14-1125</mixed-citation></ref><ref id="B46"><label>46.</label><mixed-citation>Hedonometer (English). Retrieved from https://hedonometer.org/words/labMT-en-v2/ (accessed 18 March 2024).</mixed-citation></ref><ref id="B47"><label>47.</label><mixed-citation>BRM. Retrieved from https://github.com/meadej/twitter-sentiment-analysis?ysclid=lh0bctge 6l466946169 (accessed 18 March 2024).</mixed-citation></ref><ref id="B48"><label>48.</label><mixed-citation>ANEW. Retrieved from https://github.com/eriq-augustine/sentiment-data/blob/master/anew.csv (accessed 18 March 2024).</mixed-citation></ref><ref id="B49"><label>49.</label><mixed-citation>NRC-VAD. Retrieved from https://emilhvitfeldt.github.io/textdata/reference/lexicon_nrc_vad.html (accessed 18 March 2024).</mixed-citation></ref><ref id="B50"><label>50.</label><mixed-citation>KFU Sentiment. Retrieved from https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html (accessed 18 March 2024).</mixed-citation></ref><ref id="B51"><label>51.</label><mixed-citation>KFU Sentiment BERT. Retrieved from https://kpfu.ru/tehnologiya-sozdaniya-semanticheskih-elektronnyh.html (accessed 18 March 2024).</mixed-citation></ref><ref id="B52"><label>52.</label><mixed-citation>KartaSlovSent. Retrieved from https://kartaslov.ru (accessed 18 March 2024).</mixed-citation></ref><ref id="B53"><label>53.</label><mixed-citation>Hedonometer (Russian). Retrieved from https://hedonometer.org/words/labMT-ru-v2/ (accessed 18 March 2024).</mixed-citation></ref><ref id="B54"><label>54.</label><mixed-citation>LinisCrowd. Retrieved from http://linis-crowd.org/ (accessed 18 March 2024).</mixed-citation></ref></ref-list></back></article>
