Attention-based Emotion-assisted Sentiment Forecasting in Dialogue

Congrui Zou, Yunfei Yin, Faliang Huang

Abstract: Dialogue has received a lot of research. But there is very little research on dialogue sentiment forecasting, which aims to forecast the sentimental polarity of what the interlocutor is about to say and provides sentimental guidance for empathic dialogue generation. Since the sentence has not been spoken, the vector of the sentence can't be directly obtained. And according to cognitive science, emotions are different from sentiment, but there is an internal connection. Therefore, our paper proposes an Emotion-Assisted Sentiment Forecasting (EASF) model based on attention to integrating these goals. Our model uses attention to capture the significant content of emotions and sentiment, and emotion assistance can obtain the emotional change, then this change is used to assist in the analysis of the polarity of the sentiment. Experimental results show that EASF significantly outperforms all baselines.