Sentiment Analysis of Weibo Comments Based on Graph Neural Network

Weibo is one of the most important online social platforms. Currently, user comments are increasing rapidly, which makes data management difficult. Comments show the non-standardized and colloquial form of expression. Traditional sentiment analysis techniques are no longer applicable to unspecified sentence analysis tasks. To mitigate overreliance on text sequences, ignoring syntactic structure, and the poor interpretability of feature space that are typical of traditional classification models, a sentiment classification model based on a graph neural network (GNN) is developed in this study. For each comment text, the dependency syntax is used to construct the semantic graph of the short text. Aiming at the heterogeneity of the semantic graph, the spatial domain graph filter is designed for feature extraction. Concurrently, long short-term memory (LSTM) is used as a state updater to filter node noise. In this method, a graph neural network is used as a semantic parser to encode the syntactic dependency tree, which can extract the semantic and syntactic features of sentences concurrently. Experimental results show that GNN-LSTM has achieved superior performance in the Weibo comments dataset by achieving 95.25% accuracy and 95.22% F1 score.

Automated summary

This study develops a sentiment classification model based on a graph neural network (GNN) to analyze Weibo comments. By constructing semantic graphs and using graph filters for feature extraction, this model achieves superior performance.