Advertisement

A Hybrid Framework for Detecting Non-basic Emotions in Text

  • Abid Hussain WaniEmail author
  • Rana Hashmy
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 835)

Abstract

The task of Emotion Detection from Text has received substantial attention in the recent years. Although most of the work in this field has been conducted considering only the basic set of six emotions, yet there are a number of applications wherein the importance of non-basic emotions (like interest, engagement, confusion, frustration, disappointment, boredom, hopefulness, satisfaction) is paramount. A number of applications like student feedback analysis, online forum analysis and product manual evaluation require the identification of non-basic emotions to suggest improvements and enhancements. In this study, we propose a hybrid framework for the detection and classification of such non-basic emotions from text. Our framework principally uses Support Vector Machine to detect non-basic emotions. The emotions which go undetected in supervised learning are attempted to be detected by using the lexical and semantic information from word2vec predictive model. The results obtained utilizing this framework are quite encouraging and comparable to state-of-the-art techniques available.

Keywords

Emotion detection Non-basic emotions Text classification Hybrid framework Human Computer Interaction 

References

  1. 1.
    Calvo, R.A., D’Mello, S.: Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1, 18–37 (2010)CrossRefGoogle Scholar
  2. 2.
    Kahou, S.E., et al.: EmoNets: multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10, 99–111 (2016)CrossRefGoogle Scholar
  3. 3.
    Soleymani, M., Asghari-Esfeden, S., Fu, Y., Pantic, M.: Analysis of EEG signals and facial expressions for continuous emotion detection. IEEE Trans. Affect. Comput. 7, 17–28 (2016)CrossRefGoogle Scholar
  4. 4.
    Aman, S., Szpakowicz, S.: Identifying expressions of emotion in text. In: Matoušek, V., Mautner, P. (eds.) TSD 2007. LNCS (LNAI), vol. 4629, pp. 196–205. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74628-7_27CrossRefGoogle Scholar
  5. 5.
    Balahur, A., Tanev, H.: Detecting implicit expressions of affect from text using semantic knowledge on common concept properties. In: Tenth International Conference on Language Resources and Evaluation, LREC 2016, pp. 1165–1170 (2016)Google Scholar
  6. 6.
    Canales, L., Strapparava, C., Boldrini, E., Martnez-Barco, P.: Exploiting a bootstrapping approach for automatic annotation of emotions in texts. In: Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, pp. 726–734 (2016)Google Scholar
  7. 7.
    Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)CrossRefGoogle Scholar
  8. 8.
    D’Mello, S., Calvo, R.A.: Beyond the basic emotions. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems on - CHI EA 2013, p. 2287. ACM Press, New York (2013)Google Scholar
  9. 9.
    Wani, A., Hashmy, R.: An unsupervised common sense-based learning framework for emotion detection and classification in textual social data. J. Artif. Intell. Res. Adv. 4, 49–56 (2017)Google Scholar
  10. 10.
    Oza, K.S., Kamat, R.K., Naik, P.G.: Student feedback analysis: a neural network approach. Presented at the 25 March 2017Google Scholar
  11. 11.
    Binali, H., Wu, C., Potdar, V.: Computational approaches for emotion detection in text. In: 4th IEEE International Conference on Digital Ecosystems and Technologies, pp. 172–177. IEEE (2010)Google Scholar
  12. 12.
    Luo, W., Liu, F., Liu, Z., Litman, D.: Automatic summarization of student course feedback. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 80–85 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of KashmirSrinagarIndia

Personalised recommendations