Abstract
In the recent years, microblogging services, as Twitter, have become a popular tool for expressing feelings, opinions, broadcasting news, and communicating with friends. Twitter users produced more than 340 million tweets per day which may be consider a rich source of user information. We take a supervised approach to the problem, but leverage existing hashtags in Twitter for building our training data. Finally, we tested the Spanish emotional corpus applying two different machine learning algorithms for emotion identification reaching about 65% accuracy.
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References
Picard, R.: Affective computing (1997)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.: Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine 18(1), 32–80 (2001)
Soyel, H., Demirel, H.: Facial expression recognition using 3D facial feature distances. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 831–838. Springer, Heidelberg (2007)
Reilly, J., Ghent, J., McDonald, J.: Modelling, classification and synthesis of facial expressions. In: Affective Computing: Emotion Modelling, Synthesis and Recognition, pp. 107–132
Wang, W., Chen, L., Thirunarayan, K., Sheth, A.: Harnessing Twitter ’Big Data’ for Automatic Emotion Identification (2012), knoesis.wright.edu
Blázquez Gil, G., Berlanga, A., Molina, J.: Incontexto: Multisensor architecture to obtain people context from smartphones. International Journal of Distributed Sensor Networks 2012 (2012)
Ekman, P., Friesen, W.: Facial action coding system: A technique for the measurement of facial movement (1978)
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
Derks, D., Bos, A., Von Grumbkow, J.: Emoticons and online message interpretation. Social Science Computer Review 26(3), 379–388 (2008)
Schlosberg, H.: Three dimensions of emotion. Psychological Review 61(2), 81 (1954)
Petrovic, S., Osborne, M., Lavrenko, V.: The Edinburgh twitter corpus. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media, pp. 25–26 (2010)
Shaver, P., Schwartz, J., Kirson, D., O’Connor, C.: Emotion knowledge: further exploration of a prototype approach. Journal of Personality and Social Psychology 52(6), 1061 (1987)
Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics 35(3), 399–433 (2009)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC, vol. 2010 (2010)
De Choudhury, M., Counts, S., Gamon, M.: Not all moods are created equal! exploring human emotional states in social media. In: Sixth International AAAI Conference on Weblogs and Social Media (2012)
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Gil, G.B., de Jesús, A.B., Lopéz, J.M.M. (2013). Combining Machine Learning Techniques and Natural Language Processing to Infer Emotions Using Spanish Twitter Corpus. In: Corchado, J.M., et al. Highlights on Practical Applications of Agents and Multi-Agent Systems. PAAMS 2013. Communications in Computer and Information Science, vol 365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38061-7_15
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DOI: https://doi.org/10.1007/978-3-642-38061-7_15
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