Fine-Grained Emotion Detection in Contact Center Chat Utterances

  • Shreshtha MundraEmail author
  • Anirban Sen
  • Manjira Sinha
  • Sandya Mannarswamy
  • Sandipan Dandapat
  • Shourya Roy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


Contact center chats are textual conversations involving customers and agents on queries, issues, grievances etc. about products and services. Contact centers conduct periodic analysis of these chats to measure customer satisfaction, of which the chat emotion forms one crucial component. Typically, these measures are performed at chat level. However, retrospective chat-level analysis is not sufficiently actionable for agents as it does not capture the variation in the emotion distribution across the chat. Towards that, we propose two novel weakly supervised approaches for detecting fine-grained emotions in contact center chat utterances in real time. In our first approach, we identify novel contextual and meta features and treat the task of emotion prediction as a sequence labeling problem. In second approach, we propose a neural net based method for emotion prediction in call center chats that does not require extensive feature engineering. We establish the effectiveness of the proposed methods by empirically evaluating them on a real-life contact center chat dataset. We achieve average accuracy of the order 72.6% with our first approach and 74.38% with our second approach respectively.


Emotion detection Contact center chat utterances 


  1. 1.
    Agrawal, A., An, A.: Unsupervised emotion detection from text using semantic and syntactic relations. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 1, pp. 346–353. IEEE (2012)Google Scholar
  2. 2.
    Canales, L.: Emotion detection from text: a survey (2014)Google Scholar
  3. 3.
    Chaumartin, F.R.: Upar7: a knowledge-based system for headline sentiment tagging. In: Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 422–425. SemEval 2007, Association for Computational Linguistics, Stroudsburg (2007).
  4. 4.
    Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70(4), 213 (1968)CrossRefGoogle Scholar
  5. 5.
    Ekman, P., Keltner, D.: Universal facial expressions of emotion. Calif. Ment. Health Res. Dig. 8(4), 151–158 (1970)Google Scholar
  6. 6.
    Kim, S.M., Valitutti, A., Calvo, R.A.: Evaluation of unsupervised emotion models to textual affect recognition. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, pp. 62–70. CAAGET 2010, Association for Computational Linguistics, Stroudsburg (2010).
  7. 7.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint (2014). arXiv:1412.6980
  8. 8.
    Krcadinac, U., Pasquier, P., Jovanovic, J., Devedzic, V.: Synesketch: an open source library for sentence-based emotion recognition. IEEE Trans. Affect. Comput. 4(3), 312–325 (2013)CrossRefGoogle Scholar
  9. 9.
    Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)Google Scholar
  10. 10.
    Ma, C., Prendinger, H., Ishizuka, M.: Emotion estimation and reasoning based on affective textual interaction. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 622–628. Springer, Heidelberg (2005). doi: 10.1007/11573548_80 CrossRefGoogle Scholar
  11. 11.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
  12. 12.
    Mohammad, S.M.: Emotional tweets. In: Proceedings of the First Joint Conference on Lexical and Computational Semantics, vol. 1: Proceedings of the Main Conference and the Shared Task, vol. 2: Proceedings of the Sixth International Workshop on Semantic Evaluation, pp. 246–255. Association for Computational Linguistics (2012)Google Scholar
  13. 13.
    Mostafa Al Masum, S., Prendinger, H., Ishizuka, M.: Emotion sensitive news agent: an approach towards user centric emotion sensing from the news. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pp. 614–620. WI 2007. IEEE Computer Society (2007).
  14. 14.
    Munezero, M.D., Montero, C.S., Sutinen, E., Pajunen, J.: Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text. IEEE Trans. Affect. Comput. 5(2), 101–111 (2014)CrossRefGoogle Scholar
  15. 15.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: Recognition of affect, judgment, and appreciation in text. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 806–814. COLING 2010. Association for Computational Linguistics, Stroudsburg (2010).
  16. 16.
    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)Google Scholar
  17. 17.
    Picard, R.W.: Affective computing (1995)Google Scholar
  18. 18.
    Plutchik, R.: Emotions and Life: Perspectives from Psychology, Biology, and Evolution. American Psychological Association, Ann Arbor (2003)Google Scholar
  19. 19.
    Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1556–1560. ACM (2008)Google Scholar
  20. 20.
    Wu, Y., Kita, K., Matsumoto, K., Kang, X.: A joint prediction model for multiple emotions analysis in sentences. In: Gelbukh, A. (ed.) CICLing 2013. LNCS, vol. 7817, pp. 149–160. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37256-8_13 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shreshtha Mundra
    • 1
    Email author
  • Anirban Sen
    • 2
  • Manjira Sinha
    • 1
  • Sandya Mannarswamy
    • 1
  • Sandipan Dandapat
    • 3
  • Shourya Roy
    • 4
  1. 1.Conduent LabsBangaloreIndia
  2. 2.CSEIIT DelhiNew DelhiIndia
  3. 3.Microsoft IDCBangaloreIndia
  4. 4.Big Data Labs, American ExpressNew York CityUSA

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