Abstract
Emotion plays a vital role in social interaction. Often, the speaker’s attitude is as essential as, if not more important than, his/her words for communication purposes. In this paper, we present experiments for using conversational context to help text-based emotion detection. We used data from the Dialogue Emotion Recognition Challenge – EmotionX. BERT is used for encoding the input sentences. We explore four ways for encoding the input by varying whether to concatenate a dialogue history with the current sentence and whether to add the speaker’s name as part of the input. Our results indicate that adding context can improve the results of emotion detection when the emotion categories do not overlap with each other.
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References
Wang, Y., et al.: Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (2016)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. arXiv preprint arXiv:1605.08900 (2016)
Nakov, P., et al.: SemEval-2016 task 4: Sentiment analysis in Twitter. arXiv preprint arXiv:1912.01973 (2019)
Hussein, D.M., Mohamed, E.-D.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. 30(4), 330–338 (2018)
Chatterjee, A., et al.: SemEval-2019 task 3: EmoContext contextual emotion detection in text. In: Proceedings of the 13th International Workshop on Semantic Evaluation (2019)
Bandhakavi, A., et al.: Lexicon generation for emotion detection from text. IEEE Intell. Syst. 32(1), 102–108 (2017)
Seyeditabari, A., Tabari, N., Zadrozny, W.: Emotion detection in text: a review. arXiv preprint arXiv:1806.00674 (2018)
Scherer, K.R., Wallbott, H.: International survey on emotion antecedents and reactions (isear) (1990). (2017)
Strapparava, C., Mihalcea, R.: Semeval-2007 task 14: affective text. In: Proceedings of the Fourth International Workshop on Semantic Evaluations (SemEval-2007) (2007)
Alm, C.O., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural language Processing. Association for Computational Linguistics (2005)
Hsu, C.-C., Ku, L.-W.: SocialNLP 2018 emotionX challenge overview: recognizing emotions in dialogues. In: Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media (2018)
Shmueli, B., Ku, L.-W.: SocialNLP EmotionX 2019 challenge overview: predicting emotions in spoken dialogues and chats. arXiv preprint arXiv:1909.07734 (2019)
Sim, J., Wright, C.C.: The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys. Ther. 85(3), 257–268 (2005)
Balahur, A., Hermida, J.M., Montoyo, A.: Detecting implicit expressions of sentiment in text based on commonsense knowledge. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. Association for Computational Linguistics (2011)
Sykora, M.D., et al.: Emotive ontology: extracting fine-grained emotions from terse, informal messages. In: Proceedings of the IADIS International Conference Intelligent Systems and Agents 2013, ISA 2013, Proceedings of the IADIS European Conference on Data Mining 2013, ECDM 2013 (2013)
Purver, M., Battersby, S.: Experimenting with distant supervision. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, pp. 482–491. Association for Computational Linguistics (2012)
Strapparava, C., Valitutti, A.: Wordnet affect: an affective extension of wordnet. In: Lrec, vol. 4. no. 1083–1086 (2004)
Esuli, A., Sebastiani, F.: SentiWordNet: a high-coverage lexical resource for opinion mining. Evaluation 17(1), 26 (2007)
Devlin, J., et al.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Khosla, S.: EmotionX-AR: CNN-DCNN autoencoder based emotion classifier. In: Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media (2018)
Wolf, T., et al.: Huggingface’s transformers: state-of-the-art natural language processing. ArXiv, abs/1910.03771 (2019)
Gallé, M.: Investigating the effectiveness of BPE: the power of shorter sequences. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (2019)
Huang, Y.-H., et al.: EmotionX-IDEA: emotion BERT–an affectional model for conversation. arXiv preprint arXiv:1908.06264 (2019)
Yang, Z., et al.: XLNET: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems (2019)
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Si, M. (2020). Using Context to Help Predict Speaker’s Emotions in Social Dialogue. In: Stephanidis, C., et al. HCI International 2020 – Late Breaking Papers: Interaction, Knowledge and Social Media. HCII 2020. Lecture Notes in Computer Science(), vol 12427. Springer, Cham. https://doi.org/10.1007/978-3-030-60152-2_33
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DOI: https://doi.org/10.1007/978-3-030-60152-2_33
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