Emotions Relationship Modeling in the Conversation-Level Sentiment Analysis

Jieying Xue, Minh-Phuong Nguyen, Le Minh Nguyen

Abstract: Sentiment analysis, also called opinion mining, is a task of Natural Language Processing (NLP) that aims to extract sentiments and opinions from texts. Among them, emotion recognition in conversation (ERC) is becoming increasingly popular as a new research topic in natural language processing (NLP). The current state-of-the-art models focus on injecting prior knowledge via an external commonsense extractor or applying pre-trained language models to construct the utterance vector representation that is fused with the surrounding context in a conversation. However, these architectures treat the emotional states as sequential inputs, thus omitting the strong relationship between emotional states of discontinuous utterances, especially in long conversations. To solve this problem, we propose a new architecture, Long-range dependencY emotionS Model (LYSM) to generalize the dependencies between emotional states using the self-attention mechanism, which reinforces the emotion vector represe

External IDs:dblp:conf/icaart/XueN023