PHARN: A Probabilistic Graph Model Based Hierarchical Affective Reasoning Network for Conversational Sentiment Analysis

Conversational Sentiment analysis (CSA) is an import task in the text sentiment analysis filed, which aims to predict the sentiment sequence corresponding to the sentence sequence in the conversation. Recently, many researchers have proposed deep learning based models to learn both speaker-context and sentence-context information interactions. However, the existing models generally learn the sentiment-context interactions by simple non-linear function, which does not focus on affective reasoning and is lack of interpretability. To address those issues, we introduce probabilistic graph model (PGM). Inspired by the interaction matrix of the Coupled HMM, we propose a novel interaction matrix based improvement for output gating mechanism, which contains local 2-order sentiment-context interaction. Then we propose the interaction matrix Based Long Short-Term Memory Network (ConvLSTM), which captures local sentiment-context interactions. Next, inspired by the reasoning of PGM, we develop a novel PGM layer to model global high-order sentiment-context interaction for sentiment label sequence. Finally, we propose PGM Based Hierarchical Affective Reasoning Network (PHARN), which can learn both local and global sentiment-context interaction. Experiments on MELD and IEMOCAP datasets demonstrate the effectiveness of our model, which achieves excellent results on multiple benchmarks.