Abstract
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.
(During the research, all authors were part of Conduent Labs India).
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Notes
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Utterances here referred to textual communications corresponding to each turn from either of the parties.
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We have used open source implementation CRF++ http://taku910.github.io/crfpp.
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References
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)
Canales, L.: Emotion detection from text: a survey (2014)
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). http://dl.acm.org/citation.cfm?id=1621474.1621568
Cohen, J.: Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit. Psychol. Bull. 70(4), 213 (1968)
Ekman, P., Keltner, D.: Universal facial expressions of emotion. Calif. Ment. Health Res. Dig. 8(4), 151–158 (1970)
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). http://dl.acm.org/citation.cfm?id=1860631.1860639
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint (2014). arXiv:1412.6980
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)
Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)
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
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint (2013). arXiv:1301.3781
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)
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). http://dx.doi.org/10.1109/WI.2007.129
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)
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). http://dl.acm.org/citation.cfm?id=1873781.1873872
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)
Picard, R.W.: Affective computing (1995)
Plutchik, R.: Emotions and Life: Perspectives from Psychology, Biology, and Evolution. American Psychological Association, Ann Arbor (2003)
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)
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
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Mundra, S., Sen, A., Sinha, M., Mannarswamy, S., Dandapat, S., Roy, S. (2017). Fine-Grained Emotion Detection in Contact Center Chat Utterances. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_27
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