Non-Verbal Emotion Markers in the Sentiment Analysis of Russian-Language Internet Texts
DOI:
https://doi.org/10.37482/2227-6564-V038Keywords:
Russian-language Internet texts, text classifier, machine learning, non-verbal emotion markers, sentiment analysisAbstract
This article describes the initial stages of the project aiming to design a classifier of Internet texts in Russian by emotional tonality. To create a sentiment analysis algorithm that attributes texts to one of the 8 basic emotions according to Lövheim’s cube model, it is necessary to do the following: carefully select the language material for the training sample; label its tonality with the assistance of an independent expert; carry out an expert linguistic analysis of the data in order to determine the emotion markers; validate the markers using corpus analysis tools; and, subject to their quantitative significance in the emotion corpora, validate them in the work of the prototype classifier. The author examined the possibility of using non-verbal emotion markers as classification parameters. The linguistic analysis revealed two potential parameters: lexemes written in capital letters and numbers written in figures. Double validation of the markers identified allows us to determine which of them improves the accuracy of classification. The marker of writing numbers in figures leads to a 2 % increase in the overall accuracy of the sentiment analysis algorithm, as well as to a 7 % increase in the classification accuracy for the basic emotion of interest/excitement, and a 3 % increase for the basic emotions of surprise/startle and enjoyment/joy. It is noted that non-verbal markers are slightly less effective for the sentiment analysis of texts than lexical, semantic or punctuation markers, but are as much effective as syntactic markers. The results indicate the need to consider this type of markers along with verbal markers of emotions and explore in more detail concrete non-verbal markers as potential classifier parameters.
Downloads
References
Hogenboom A., Frasincar F., de Jong F., Kaymak U. Polarity Classification Using Structure-Based Vector Representations of Text // Decis. Support Sys. 2015. № 74. Р. 46–56.
Loukachevitch N.V., Blinov P.D., Kotelnikov E.V., Rubtsova Y.V., Ivanov V.V., Tutubalina E. SentiRuEval: Testing Object-Oriented Sentiment Analysis Systems in Russian // Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue”, Moscow, 27–30 May 2015. Мoscow: RSUH, 2015. Iss. 14 / ed. by V.P. Selegey. P. 3–15.
Vasilyev V.G., Denisenko A.A., Soloviev D.A. Aspect Extraction and Twitter Sentiment Classification by Fragment Rules// Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue”,Moscow, 27–30 May 2015. Мoscow: RSUH, 2015. Iss. 14 / ed. by V.P. Selegey. P. 76–88.
Karpov I.A., Kozhevnikov M.V., Kazorin V.I., Nemov N.R. Entity Based Sentiment Analysis Using Syntax Patterns and Convolutional Neural Network // Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue”, Moscow, 1–4 July 2016. Мoscow: RSUH, 2016. Iss. 15 / ed. by V.P. Selegey. P. 225–236.
Lucas G.M., Gratch J., Malandrakis N., Szablowski E., Fessler E., Nichols J. GOAALLL!: Using Sentiment in the World Cup to Explore Theories of Emotion // Image Vis. Comput. 2017. Vol. 65. P. 58–65.
Staiano J., Guerini M. DepecheMood: A Lexicon for Emotion Analysis from Crowd-Annotated News // Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), Baltimore, USA, 22–27 June 2014 / ed. by K. Toutanova, H. Wu. N. Y.: Association for Computational Linguistics, 2014. P. 427–433.
Alm C.O., Roth D., Sproat R. Emotions from Text: Machine Learning for Text-Based Emotion Prediction //
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, Canada, 6–8 October 2005 / ed. by R.J. Mooney. Stroudsburg: Association for Computational Linguistics, 2005. P. 579–586.
Lövheim H. A New Three-Dimensional Model for Emotions and Monoamine Neurotransmitters //Medical Hypotheses. 2012. Vol. 78, № 2. Р. 341–348.
Tomkins S.S.Affect Theory // Emotion in the Human Face / ed. by P. Ekman. Cambridge: Cambridge University Press, 1982. P. 353–395.
Potapova R., Lykova O. Verbal Representation of Lies in Russian and Anglo-American Cultures // Procedia – Soc. Behav. Sci. 2016. Vol. 236. P. 114–118.
Pisarevskaya D. Rhetorical Structure Theory as a Feature for Deception Detection in News Reports in the Russian Language // Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue”, Moscow, 31 May – 3 June 2017. Мoscow: RSUH, 2017. Vol. 1, iss. 16 / ed. by V.P. Selegey. P. 191–200.
Potapova R., Komalova L. Multimodal Perception of Aggressive Behavior // Lecture Notes in Computer Science. Cham: Springer, 2016. Vol. 9811. P. 499–506.
Koltsova O.Y., Alexeeva S.V., Kolcov S.N. An Opinion Word Lexicon and a Training Dataset for Russian
Sentiment Analysis of Social Media // Computational Linguistics and Intellectual Technologies: Proceedings of the Annual International Conference “Dialogue”, Moscow, 1–4 July 2016. Мoscow: RSUH, 2016. Iss. 15 / ed. by V.P. Selegey. P. 259–268.
Колосов Я.В. Лингвистические корреляты эмоционального состояния «страх» в русской и английской речи: формирование базы данных: дис. … канд. филол. наук. М., 2004. 214 c.
Колмогорова А.В. Вербальные маркеры эмоций в контексте решения задач сентимент-анализа // Вопр. когнит. лингвистики. 2018. № 1(54). С. 83–93. DOI: 10.20916/1812-3228-2018-1-83-93
Kolmogorova A., Kalinin A., Malikova A. Emojis as Predictors in Lövheim Cube Backed Multi-Class Sentiment Analysis: Can We Really Trust Them? // 6th SWS International Scientific Conference on Arts and Humanities ISCAH 2019. Sofia, 2019. Vol. 6. P. 645–652.
