EmoTwitter – A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets

  • Myriam MunezeroEmail author
  • Calkin Suero Montero
  • Maxim Mozgovoy
  • Erkki Sutinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)


Traditionally, work on sentiment analysis focuses on detecting the positive and negative attributes of sentiments. To broaden the scope, we introduce the concept of enduring sentiments based on psychological descriptions of sentiments as enduring emotional dispositions that have formed over time. To aid us identify the enduring sentiments, we present a fine-grained functional visualization system, EmoTwitter, that takes tweets written over a period of time as input for analysis. Adopting a lexicon-based approach, the system identifies the Plutchik’s eight emotion categories and shows them over the time period that the tweets were written. The enduring sentiment patters of like and dislike are then calculated over the time period using the flow of the emotion categories. The potential impact and usefulness of our system are highlighted during a user-based evaluation. Moreover, the new concept and technique introduced in this paper for extracting enduring sentiments from text shows great potential, for instance, in business decision making.


Sentiment Analysis Emotions Enduring 


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  1. 1.
    Broad, C.D.: Emotion and sentiment. Journal of Aesthetics and Art Criticism 13(2), 203–214 (1971)CrossRefGoogle Scholar
  2. 2.
    Cattell, R.B.: Sentiment or attitude? the core of a terminology problem in personality research. Journal of Personality 9, 6–17 (2006)CrossRefGoogle Scholar
  3. 3.
    Duan, L., Xu, D., Tsang, I.W.: Learning with augmented features for heterogeneous domain adaptation. In: Proc. of the 29th International Conference on Machine Learning, pp. 711–718. Omnipress, Edinburgh (2012)Google Scholar
  4. 4.
    Esuli, A., Sebastiani, F.: Sentiwordnet: A publicly available lexical resource for opinion mining. In: Proc. of the 5th Conference on Language Resources and Evaluation (LREC), Genoa, Italy, pp. 512–422 (2006)Google Scholar
  5. 5.
    French, V.V.: The structure of sentiments: I. A restatement of the theory of sentiments. Journal of Personality 15(4), 247–287 (1947)CrossRefGoogle Scholar
  6. 6.
    Fukuhara, T., Nakagawa, H., Nishida, T.: Understanding sentiment of people from news articles: Temporal sentiment analysis of social events. In: Proc. of the International Conference on Weblogs and Social Media (ICWSM), Boulder, Colorado, USA (2007)Google Scholar
  7. 7.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Tech. rep., Technical report, Stanford Digital Library Technologies Project (2009)Google Scholar
  8. 8.
    Gordon, S.L.: The sociology of sentiments and emotion. In: Rosenberg, M., Turner, R.H. (eds.) Social Psychology: Sociological Perspectives, pp. 562–592. Basic Books, New York (1981)Google Scholar
  9. 9.
    Havre, S., Hetzler, B., Nowell, L.: Themeriver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualizition and Computer Graphics 8(1), 9–20 (2002)CrossRefGoogle Scholar
  10. 10.
    Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: Proc. of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal, pp. 703–710 (2013)Google Scholar
  11. 11.
    Kempter, R., Sintsova, V., Musat, C., Pu, P.: Emotionwatch: Visualizing fine-grained emotion in event-related tweets. In: Proc. of the 8th International AAAI Conference on Weblogs and Social Media (2014)Google Scholar
  12. 12.
    Kim, S., Hovy, E.: Determining the sentiment of opinions. In: Proc. 20th International Conference on Computational Linguistics (COLING 2004). ACL, Stroudsburg (2004)Google Scholar
  13. 13.
    Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Tweets as data: Demonstration of tweeql and twitinfo. In: Proc. of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD 2011), pp. 1259–1262 (2011)Google Scholar
  14. 14.
    Martínez-Cámara, E., Martín-Valdiva, T.M., Ureña-López, A.L., Montejo-Ráez, A.: Sentiment analysis in twitter. Journal of Natural Language Engineering 20(1), 1–28 (2014)CrossRefGoogle Scholar
  15. 15.
    Mishne, G., de Rijke, M.: Moodviews: Tools for blog mood analysis. In: AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, pp. 153–154 (2006)Google Scholar
  16. 16.
    Mohammad, S.M., Turney, P.D.: Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. In: Proc. of the NAACL-HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, LA, California, pp. 26–34 (2010)Google Scholar
  17. 17.
    Munezero, M., Montero, S.C., Mozgovoy, M., Sutinen, E., Pajunen, J.: Are they different? affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Transaction on Affective Computing 5(2), 101–111 (2014)CrossRefGoogle Scholar
  18. 18.
    Murray, H.A., Morgan, C.D.: A clinical study of sentiments i and ii. Genetic Psychological Monographs 32(1-2),3–149, 153–311 (1945)Google Scholar
  19. 19.
    OxfordDictionaries: Enduring. Oxford University Press (2014)Google Scholar
  20. 20.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  21. 21.
    Plutchik, R.: A general psychoevolutionary theory of emotion. Emotion 1(3), 3–33 (1980)Google Scholar
  22. 22.
    Porter, M.F.: An Algorithm for Suffix Stripping. Cambridge University Press, Cambridge (1980)Google Scholar
  23. 23.
    Robinson, D.T., Smith-Lovin, L., Wisecu, A.K.: Symbolic interactionist roots of affect control theory. In: Stets, J.E., Turner, J.H. (eds.) Handbook of the Sociology of Emotions, pp. 179–199. Springer, New York (2007)Google Scholar
  24. 24.
    Saif, Y.H., Fernandez, M., Alani, H.: Evaluation datasets for twitter sentiment analysis: A survey and a new dataset, the sts-gold. In: CEUR Workshop Proceedings of ESSEM Workshop, pp. 9–21 (2013)Google Scholar
  25. 25.
    Scherer, K.S.: What are emotions? and how can they be measured? Social Science Information 44(4), 693–727 (2005)CrossRefGoogle Scholar
  26. 26.
    Shelly, R.K.: Emotions, sentiments and performance expectations. In: Turner, J. (ed.) Theory and Research on Human Emotions: Advances in Group Processes, vol. 21, pp. 141–165. Emerald Group Publishing Limited (2004)Google Scholar
  27. 27.
    Strapparava, C., Valitutti, A.: Wordnet-affect: An affective extension of wordnet. In: Proc. of the 4th International Conference on Language Resources and Evaluation, pp. 1413–1418 (2004)Google Scholar
  28. 28.
    Thelwall, M.: Sentiment analysis and time series with twitter. In: Weller, K., Bruns, A., Burgess, J., Mahrt, M., Puschmann, C. (eds.) Twitter and Society, pp. 83–96. Peter Lang, New York (2014)Google Scholar
  29. 29.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Language Resources and Evaluation 39(2-3), 165–210 (2005)CrossRefGoogle Scholar
  30. 30.
    Wilson, T., Wiebe, J.: Annotating attributions and private states. In: Proc. of the ACL Workshop on Frontiers in Corpus Annotation II, Pie in the Sky, pp. 53–60 (2005)Google Scholar
  31. 31.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences, pp. 129–136 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Myriam Munezero
    • 1
    Email author
  • Calkin Suero Montero
    • 1
  • Maxim Mozgovoy
    • 2
  • Erkki Sutinen
    • 1
  1. 1.School of ComputingUniversity of Eastern FinlandJoensuuFinland
  2. 2.The University of AizuAizu-wakamatsuFukushima

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