Sentiment Forecasting in Dialog

Proceedings of the 28th International Conference on Computational Linguistics, pages 2448–2458 Barcelona, Spain (Online), December 8-13, 2020 2448 Sentiment Forecasting in Dialog Zhongqing Wang†, Xiujun Zhu†, Yue Zhang‡, Shoushan Li†, and Guodong Zhou† † Natural Language Processing Lab, Soochow University, Suzhou, China ‡ Westlake University, Hangzhou, China wangzq@suda.edu.cn, 1300821865@qq.com yue.zhang@wias.org.cn, {lishoushan, gdzhou}@suda.edu.cn Abstract Sentiment forecasting in dialog aims to predict the polarity of next utterance to come, and can help speakers revise their utterances in sentimental utterances generation. However, the polar- ity of next utterance is normally hard to predict, due to the lack of content of next utterance (yet to come). In this study, we propose a Neural Sentiment Forecasting (NSF) model to ad- dress inherent challenges. In particular, we employ a neural simulation model to simulate the next utterance based on the context (previous utterances encountered). Moreover, we employ a sequence influence model to learn both pair-wise and seq-wise influence. Empirical studies illus- trate the importance of proposed sentiment forecasting task, and justify the effectiveness of our NSF model over several strong baselines. 1 Introduction Developing intelligent chatbots is of great appealing to both the industry and the academics. However it is challenging to build up such an intelligent chatbot which involves a series of high-level natural language processing techniques, such as sentiment analysis of utterances in dialog. Previous studies on sentiment classification focus on determining polarity (positive or negative) in a single document (Pang and Lee, 2008; Amplayo et al., 2018). In comparison, only few studies focus on determining polarity of utterances in dialog (Herzig et al., 2016; Majumder et al., 2018). However, all of these studies focus on determining the polarity of existing utterances. It may be more important to predict the polarity of next utterance yet to come. Given the example in Figure 1, although B expresses a positive sentiment in second utterance, A still shows a negative sentiment in his response. In this case, if B know that A would be very upset after his first utterance, he may revise his utterance to let A feel more comfortable. Hence, predicting the polarity of the next utterance can help a speaker to improve their utterances, which is important in automatic dialogue, such as customer service. To the above purpose, we propose a new task, calls sentiment forecasting in dialog, which aims to predict the polarity of next utterance yet to come. In this paper, we focus on tackling two inherent challenges, one is how to simulate next utterance, and the other is how to learn the influence from context towards next utterance. The motivations behind are that, since next utterance is yet to come, it would be helpful if we can simulate next utterance from the context. Moreover, the polarity of next utterance can be much influenced by the context, it is necessary to consider the influence of the context for next utterance. In this paper, we propose a Neural Sentiment Forecasting (NSF) model to address above challenges. In particular, a neural simulation model is …

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