Joint Syntax-Enhanced and Topic-Driven Graph Networks for Emotion Recognition in Multi-Speaker Conversations

Daily conversations contain rich emotional information, and identifying this emotional information has become a hot task in the field of natural language processing. The traditional dialogue sentiment analysis method studies one-to-one dialogues and cannot be effectively applied to multi-speaker dialogues. This paper focuses on the relationship between participants in a multi-speaker conversation and analyzes the influence of each speaker on the emotion of the whole conversation. We summarize the challenges of emotion recognition work in multi-speaker dialogue, focusing on the context-topic switching problem caused by multi-speaker dialogue due to its free flow of topics. For this challenge, this paper proposes a graph network that combines syntactic structure and topic information. A syntax module is designed to convert sentences into graphs, using edges to represent dependencies between words, solving the colloquial problem of daily conversations. We use graph convolutional networks to extract the implicit meaning of discourse. In addition, we focus on the impact of topic information on sentiment, so we design a topic module to optimize the topic extraction and classification of sentences by VAE. Then, we use the combination of attention mechanism and syntactic structure to strengthen the model's ability to analyze sentences. In addition, the topic segmentation technology is adopted to solve the long-term dependencies problem, and a heterogeneous graph is used to model the dialogue. The nodes of the graph combine speaker information and utterance information. Aiming at the interaction relationship between the subject and the object of the dialogue, different edge types are used to represent different interaction relationships, and different weights are assigned to them. The experimental results of our work on multiple public datasets show that the new model outperforms several other alternative methods in sentiment label classification results. In the multi-person dialogue dataset, the classification accuracy is increased by more than 4%, which verifies the effectiveness of constructing heterogeneous dialogue graphs.