Exploiting Emoticons to Generate Emotional Dictionaries from Facebook Pages

  • Hanen AmeurEmail author
  • Salma Jamoussi
  • Abdelmajid Ben Hamadou
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)


During the first events of the Tunisian revolution, the social network, Facebook, played a key role in Tunisia and everywhere in the world. It became the first political tool that allows the Tunisian people to share trending news in actual time. Facebook provides the opportunity for users to comment on the news by expressing their sentiments. In this paper, we focus on emotion analysis of Tunisian Facebook pages. To do this, we first collect comments from the Facebook pages in order to analyze sentiments written in Tunisian dialect. Then, we propose a new method for emotional dictionaries construction. In fact, we distinguish nine emotional classes: surprised, satisfied, happy, gleeful, romantic, disappointed, sad, angry and disgusted. At this step, we focus on the use of emotion symbols as indicators of sentiment polarity. Finally, we present the experimental results of our method. Our system achieves effective and consistent results.


Sentiment analysis Emotion analysis Emotional dictionaries Tunisian dialect Emotion symbols Political lexicon 


  1. 1.
    Abdul-Mageed, M., Diab, M.: Sana: a large scale multi-genre, multi-dialect Lexicon for Arabic subjectivity and sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). ELRA, Reykjavik, Iceland (2014)Google Scholar
  2. 2.
    Alena, N., Helmut, P., Mitsuru, I.: Analysis of affect expressed through the evolving language of online communication. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, pp. 278–281. ACM, New York, NY, USA (2007)Google Scholar
  3. 3.
    Ameur, H., Jamoussi, S.: Dynamic construction of dictionaries for sentiment classification. In: 13th IEEE International Conference on Data Mining Workshops. ICDM Workshops, pp. 896–903. TX, USA (2013)Google Scholar
  4. 4.
    Balabantaray, R.C., Mohammad, M., Sharma, N.: Article: Multi-class twitter emotion classification: a new approach. Int. J. Appl. Inf. Syst. 4(1), 48–53 (2012)Google Scholar
  5. 5.
    Diab, M., Albadrashiny, M., Aminian, M., Attia, M., Elfardy, H., Habash, N., Hawwari, A., Salloum, W., Dasigi, P., Eskander, R.: Tharwa: A large scale dialectal Arabic—standard Arabic—English Lexicon. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14). ELRA, Reykjavik, Iceland (2014)Google Scholar
  6. 6.
    Douglas, R.R., Christopher, Z.: Corpus-based dictionaries for sentiment analysis of specialized vocabularies. In: New Directions in Analyzing Text as DataWorkshop (2013)Google Scholar
  7. 7.
    Duyu, T., Bing, Q., Ting, L., Zhenghua, L.: Learning sentence representation for emotion classification on microblogs. In: Natural Language Processing and Chinese Computing—Second CCF Conference, pp. 212–223. Chongqing, China (2013)Google Scholar
  8. 8.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6, 169–200 (1992)CrossRefGoogle Scholar
  9. 9.
    Kamps, J., Marx, M.: Words with attitude. In: 1st International WordNet Conference, pp. 332–341. Mysore, India (2002)Google Scholar
  10. 10.
    Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics. ACL, Stroudsburg, PA, USA (2004)Google Scholar
  11. 11.
    Mihalcea, R., Liu, H.: A corpus-based approach to finding happiness. In: Proceedings of the AAAI Spring Symposium on Computational Approaches to Weblogs (2006)Google Scholar
  12. 12.
    Mohammad, S.M.: Sentiment analysis: detecting valence, emotions, and other affectual states from text. In: Meiselman, H. (ed.) Emotion Measurement. Elsevier (2016)Google Scholar
  13. 13.
    Solakidis, G., Vavliakis, K., Mitkas, P.: Multilingual sentiment analysis using emoticons and keywords. In: 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), pp. 102–109. Warsaw, Poland (2014)Google Scholar
  14. 14.
    Taboada, M., Anthony, C., Voll, K.: Methods for creating semantic orientation dictionaries. In: Conference on Language Resources and Evaluation (LREC), pp. 427–432 (2006)Google Scholar
  15. 15.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. ACL, Stroudsburg, PA, USA (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (, which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Authors and Affiliations

  • Hanen Ameur
    • 1
    Email author
  • Salma Jamoussi
    • 1
  • Abdelmajid Ben Hamadou
    • 1
  1. 1.Multimedia InfoRmation Systems and Advanced Computing LaboratoryMIRACL-Sfax University, Sfax-Tunisia Technopole of SfaxSfaxTunisia

Personalised recommendations