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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Kahou, S.E., et al.: EmoNets: multimodal deep learning approaches for emotion recognition in video. J. Multimodal User Interfaces 10, 99–111 (2016)
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)
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_27
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)
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)
Ekman, P.: An argument for basic emotions. Cogn. Emot. 6(3–4), 169–200 (1992)
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)
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)
Oza, K.S., Kamat, R.K., Naik, P.G.: Student feedback analysis: a neural network approach. Presented at the 25 March 2017
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wani, A.H., Hashmy, R. (2019). A Hybrid Framework for Detecting Non-basic Emotions in Text. In: Minz, S., Karmakar, S., Kharb, L. (eds) Information, Communication and Computing Technology. ICICCT 2018. Communications in Computer and Information Science, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-13-5992-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-5992-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5991-0
Online ISBN: 978-981-13-5992-7
eBook Packages: Computer ScienceComputer Science (R0)