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A STUDY OF DIFFERENT TEXT EMOTION RECOGNITION TECHNIQUES


volume 5 issue 1 Download Paper
Year of Publication: 2019
Authors: Syara Bano, Ramanjot Kaur

Abstract


Emotion can be expressed in many ways, such as face expression and gestures, speech and written text. The detection of emotions from text documents is essentially a content-based classification, which used the concept of Natural Language programming (NLP) domains. In this paper, an overview of emotion recognition especially from the text document is discussed. The paper also described different techniques used for the classification of emotions. The work done by various researcher using different techniques along with the output measured is discussed.


References


[1].   Agrawal, A., & An, A. (2012, December). Unsupervised emotion detection from text using semantic and syntactic relations. In Proceedings of The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 346-353). IEEE Computer Society.

[2].   Kao, E. C. C., Liu, C. C., Yang, T. H., Hsieh, C. T., & Soo, V. W. (2009, April). Towards text-based emotion detection a survey and possible improvements. In 2009 International Conference on Information Management and Engineering (pp. 70-74). IEEE.

[3].   Canales, L., & Martínez-Barco, P. (2014). Emotion detection from text: A survey. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC) (pp. 37-43).

[4].   Danisman, T., & Alpkocak, A. (2008, April). Feeler: Emotion classification of text using vector space model. In AISB 2008 Convention Communication, Interaction and Social Intelligence (Vol. 1, p. 53).

[5].   Sailunaz, K., Dhaliwal, M., Rokne, J., & Alhajj, R. (2018). Emotion detection from text and speech: a survey. Social Network Analysis and Mining, 8(1), 28.

[6].   Jayakrishnan, R., Gopal, G. N., & Santhikrishna, M. S. (2018, January). Multi-class emotion detection and annotation in malayalam novels. In 2018 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE.

[7].   Kratzwald, B., Ilić, S., Kraus, M., Feuerriegel, S., & Prendinger, H. (2018). Deep learning for affective computing: Text-based emotion recognition in decision support. Decision Support Systems, 115, 24-35.

[8].   Agrawal, A., &An, A. (2012, December). Unsupervised emotion detection from text using semantic and syntactic relations. In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 346-353). IEEE Computer Society.

[9].   Binali, H., &Potdar, V. (2010, April). Computational approaches for emotion detection in text. In 4th IEEE International Conference on Digital Ecosystems and Technologies (pp. 172-177). IEEE

[10].   Tao, J. (2004). Context based emotion detection from text input. In Eighth International Conference on Spoken Language Processing.

[11].   Canales, L., &Martínez-Barco, P. (2014). Emotion detection from text: A survey. In Proceedings of the Workshop on Natural Language Processing in the 5th Information Systems Research Working Days (JISIC).

[12].   Hancock, J. T., Landrigan, C., & Silver, C. (2007, April).Expressing emotion in text-based communication. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 929-932). ACM.

 


Keywords


Emotions, Text Data, Keyword Spotting Method, Lexical Affinity Method, Hybrid Approach.