№1, 2018


Rasim M. Alguliyev, Irada Y. Alakbarova

The article overviews the interpretations of scientists on the theory of emotion and studies several systems analyzing the emotional tone of various types of information. A general conceptual model for assessing the emotional content of data stored in social media is given. The prospects for using Opinion Mining and Sentiment Analysis methods for analyzing emotions in the text are determined (pp.3-14).

Keywords: emotion, emotionology, social media, text tonality, Opinion Mining, Sentiment Analysis, Emoji language.
  • Martinez A., Estrada H., Molina A., Mejia M., Perez J. Emotion-Bracelet: A Web Service for Expressing Emotions through an Electronic Interface // Sensors (online), 2016, 16(12). http://mdpi.com/1424-8220/16/12/1980/
  • Darwin C.R. The expression of the emotions in man and animals. London: John Murray, 1872, 472 p.
  • Oatley K. Best Laid Schemes: the Psychology of Emotions. Cambridge University Press, 1992, 445 p.
  • Ekman P. What Scientists Who Study Emotion Agree About // Perspectives on Psychological Science, 2016, vol.11. no.1, pp.31–34.
  • Plutchik R. The Emotions. University Press of America, 1991, 216 p.
  • Izard C.E. The face of emotion. Appleton-Century-Crofts, 1971, 468 p.
  • Izard C.E. Differential emotions theory and the facial feedback hypothesis of emotion activation: Comments on Tourangeau and Ellsworth's "The role of facial response in the experience of emotion // Personality and Social Psychology, 1981, vol.40, no.2, pp.350–354.
  • Rubinstein S.L. The basics of general psychology. Publisher: Piter, 2002, 720 pp.
  • Simonov P.V. Emotional brain. M.:Nauka, 1981, 166 с.
  • Schachter S., Singer J. Cognitive. Social and Physiological Determinants of Emotional State. Psychological Review, 1962, vol.69, no.5, pp.379–399.
  • http://affect.media.mit.edu/
  • noldus.com/facereader/facereader-online/
  • Den Uyl M., Van Kuilenberg H. The facereader: online facial expression recognition / Proceedings of the 5th International Conference on Methods and Techniques in Behavioral Research, 30 August–September 2005, Netherlands, Noldus Information Technology, pp.589–590.
  • Software sees Mona Lisa’s emotions. https://geek.com/news/software-sees-mona-lisas-emotions-558144/
  • Morozov V.P. Emotional Hearing and Music Endowments / Proceedings of the International Scientific and Practical Conference "Development of the Scientific Heritage of Teplov B.M. in Domestic and World Science", Moscow: P. RAO, 2006. pp.198–203.
  • Burkhardt F., Paeschke A., Rolfes M., Sendlmeier W., Weiss B. A. Database of German Emotional Speech / Proceedings of the Interspeech, 2005, pp.1517–1520.
  • Kelly R.D. Voice Stress Analysis: Only 15 Percent of Lies About Drug Use Detected in Field Test // National İnstitute of Justice Journal, 2008, no.259, pp.8–12.
  • Haddad D., Walter S., Ratley R., Smith M. Investigation and Evaluation of Voice Stress Analysis Technology, The Final Report of Department of Justice, 2002, 120 p.
  • Nahin N.H., Mohammad A.J., Mahmud H., Hasan K. Identifying emotion by keystroke dynamics and text pattern analysis // Behaviour and Information Technology, 2014, vol.33, no.9, 987–996.
  • Bing L. Sentiment Analysis and Opinion Mining. 2012, Morgan & Claypool Publishers, 167 p.
  • Pang B., Lee L. Opinion Mining and Sentiment Analysis // Foundations and Trends in Information Retrieval, 2008, vol.2, no.1–2, 135 p.
  • Feldman R., Sanger J. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. 2006, Cambridge University Press, 410 p.
  • Wong M. Emotion Assessment in Evaluation of Affective Interfaces, 2006, www.cgl.uwaterloo.ca/wmcowan/research/essays/maria.pdf
  • Pulse of the Nation: U.S. Mood throughout the Day Inferred from Twitter, 2010. http://infosthetics.com/archives/2010/07/
  • Customer service and business results: a survey of customer service from mid-size companies, 2013. www.d16cvnquvjw7pr.cloudfront.net/
  • Piercy Symbols: A Universal Language, 2013, London:Michael O’Mara, 224 p.
  • Encyclopedia of Emoji: the meaning of all 1427 emoticons iOS 10.3. www.iphones.ru/iNotes/all-about-emoji-ios-10
  • Ljubešić N., Fiser A Global Analysis of Emoji Usage / Proceedings of the 10th Web as Corpus Workshop and the EmpiriST Shared Task, August 7-12, 2016, Berlin, pp.82–89.
  • Turney P. Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews / Proceedings of the Association for Computational Linguistics, 2002, pp.417–424.
  • Chetviorkin I.I. Testing the sentiment classification aproach in various domains / Proceedings of the International Conference “Dialog 2012”, vol.2, no.11, pp.15–27.
  • Kan D. Rule-based approach to sentiment analysis / Sentiment Analysis Track at ROMIP, 2011. http://dialog-21.ru/digests/dialog2012/materials/pdf/Kan.pdf
  • cs.uic.edu/~liub/FBS/sentiment-analysis.html
  • Paroshina I. Natural language processing, difficulties of understanding and social networks // Computer-Online, 2014.
  • Montefinese M., Ambrosini E., Fairfield B., Mammarella N. The adaptation of the Affective Norms for English Words (ANEW) for Italian // Behavior Research Methods, 2014, vol.46, 3, pp.887–903.
  • Bradley M.M., Lang P.J. Affective norms for English words (ANEW): Instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida, 1999, pp.1–45.
  • Warren D. TenHouten, A General Theory of Emotions and Social Life, 2006, Routledge, 336 p.
  • Sobkowicz, Kaschesky M., Bouchard G. Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web // Government Information Quarterly, 2012, vol.29, no.4, pp.470–479.
  • Geeraerts Theories of Lexical Semantics, Oxford University Press, 2009, 384 p.
  • Alguliyev R.M., Aliguliyev R.M., Alakbarova I.Y. Extraction of hidden social networks from wiki-environment involved in information conflict // International Journal of Intelligent Systems and Applications (IJISA), 2016, vol.8, no.2, pp.20–27.
  • Alguliev R.M., Aliguliyev R.M. Automatic text documents summarization through sentences clustering // Automation and Information Sciences, 2008, vol.40, no.9, pp.53–63.
  • Premchandani S., Pise M., Wankhede A. Artificial Intelligence and Opinion Mining // Journal of Artificial Intelligence, 2012, vol.3, no.2, pp.102–105.
  • Godbole N., Srinivasaiah M., Skiena S. Large-Scale Sentiment Analysis for News and Blogs / Proceedings of the International Conference on Weblogs and Social Media, March 26–28, 2007, pp.39–41.
  • Kanayama H, Hideo W., Nasukawa T. Deeper sentiment analysis using machine translation technology / Proceedings of the 20th International conference on Computational Linguistics, Tokyo, Japan, 2004. http://aclweb.org/anthology/C04-1071
  • Moraes R., Valiati J.F., Wilson P. Gavião N. Document-level sentiment classification: an empirical comparison between SVM and ANN // Expert Systems with Application, 2013, vol.40, issue 2, pp.621–633.
  • Hye-Jin Min, Jong C. Park. Identifying helpful reviews based on customer’s mentions about experiences // Expert Systems with Application, 2012, vol.39, issue15, pp.11830–11838.
  • Thelwall M., Buckley K., Paltoglou G., Cai D., Kappas A. Sentiment strength detection in short informal text // Journal of the American Society for Information Science and Technology, 2010, vol.61, issue 12, pp.2544–2558.
  • Yusupova N.I., Bogdanova D.R., Boyko M.V. Algorithmic support and software for the Sentiment analysis of text messages using machine learning // Mathematical modeling, numerical methods and program complexes, 2012, No6 (51), pp.91–99.
  • Poshevkin R.V., Bessmertny I.A. Application of sentiment analysis of texts for assessing public opinion // Scientific and Technical News of Information Technologies, Mechanics and Optics, 2015, Volume 15, No. 1, pp.169–171.
  • Kang H., Joon Yoo S., Han D. Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews // Expert Systems with Application, 2012, vol.39, issue 5, pp.6000–6010.
  • Wu Y., Kita K., Matsumoto K., Kang X. A Joint Prediction Model for Multiple Emotions Analysis in Sentences // Computational Linguistics and Intelligent Text Processing, 2013, vol.7817, pp.149–160.
  • Losada E., Azzopardi L. Assessing multivariate Bernoulli models for information retrieval // ACM Transactions on Information Systems, 2016, vol.26, issue 3, pp.53–65.