Abstract
People express their emotions in various ways, including facial expression, gesture, speech, speech frequency, writing, etc. In today’s world where almost every person interacts with other people via social networking and social media, the emotional state of a person can be determined by analyzing the text collected from his/her posts and comments. Although emotion extraction and analysis from text posted in social networks and social media like facebook, twitter, etc. is a very challenging task, still it can give researchers a valuable insight into the complexity of human emotions. In this paper, test from tweets has been used for detecting 32 primary human emotions and then the emotions were analyzed against gender, location, and temporal information of the considered people.
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References
Broad, C.D.: Emotion and sentiment. J. Aesthet. Art Critic. 13(2), 203–214 (1954)
Oxford dictionaries. https://en.oxforddictionaries.com/definition/
Plutchik, R.: Emotion: A Psychoevolutionary Synthesis. Harper and Row, New York (1980)
Hasan, M., Rundensteiner, E., Agu, E.: Emotex: detecting emotions in twitter messages. In: Proceedings of the Sixth ASE International Conference on Social Computing (SocialCom 2014). Academy of Science and Engineering (ASE) (2014)
Yan, J.L.S., Turtle, H.R.: Exploring fine-grained emotion detection in tweets. In: Proceedings of NAACL-HLT, pp. 73–80 (2016)
Yang, M., Peng, B., Chen, Z., Zhu, D., Chow, K.-P.: A topic model for building fine-grained domain specific emotion lexicon. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 421–426 (2014)
Bandhakavi, A., Wiratunga, N., Massie, S., Padmanabhan, D.: Lexicon generation for emotion detection from text. IEEE Intell. Syst. 32(1), 102–108 (2017)
Calvo, R.A., Kim, S.M.: Emotions in text: dimensional and categorical models. Comput. Intell. 29(3), 527–543 (2013)
Balabantaray, C., Mohammad, M., Sharma, N.: Multi-class twitter emotion classification: a new approach. Int. J. Appl. Inf. Syst. 4(1), 48–53 (2012)
Fathy, S., El-Haggar, N., Haggag, M.H.: A hybrid model for emotion detection from text. Int. J. Inf. Retr. Res. 7(1), 32–48 (2017)
Joshi, A., Tripathi, V., Soni, R., Bhattacharyya, P., Carman, M.J.: Emogram: an open-source time sequence-based emotion tracker and its innovative applications. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence (2016)
Wu, X., Bartram, L., Shaw, C.: Plexus: an interactive visualization tool for analyzing public emotions from twitter data (2017). Preprint. arXiv:1701.06270
Gui, L., Wu, D., Xu, R., Lu, Q., Zhou, Y.: Event-driven emotion cause extraction with corpus construction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1639–1649 (2016)
Wang, Z., Lee, S.Y.M., Li, S., Zhou, G.: Emotion analysis in code-switching text with joint factor graph model. In: Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, vol. 25(3), pp. 469–480 (2017)
Ed Ekman, P., Davidson, R.J.: The Nature of Emotion: Fundamental Questions. Oxford University Press, New York (1994)
Oatley, K., Johnson-Laird, P.N.: Towards a cognitive theory of emotions. Cognit. Emot. 1(1), 29–50 (1987)
Ed Plutchik, R., Conte, H.R.: Circumplex models of personality and emotions. American Psychological Association, Washington (1997)
Wordnet. https://wordnet.princeton.edu/
Cambridge dictionary. http://dictionary.cambridge.org/dictionary/
Dictionary.com. http://www.dictionary.com/
Thesaurus.com. http://www.thesaurus.com/
Wordsmyth. http://www.wordsmyth.net/
twittonary.com. http://twittonary.com/
twictionary. http://twictionary.pbworks.com/w/page/22547584/frontpage
Data for everyone. https://www.crowdflower.com/data-for-everyone/
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Sailunaz, K., Özyer, T., Rokne, J., Alhajj, R. (2018). Text-Based Analysis of Emotion by Considering Tweets. In: Özyer, T., Alhajj, R. (eds) Machine Learning Techniques for Online Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-89932-9_11
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DOI: https://doi.org/10.1007/978-3-319-89932-9_11
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