SENTIMENT ANALYSIS OF WEIBO COMMENTS BASED ON CONVOLUTIONAL NEURAL NETWORK

Abstract

Sentiment analysis of social media comments plays a crucial role in understanding public opinion and sentiment trends. This study focuses on sentiment analysis of Weibo comments, a popular microblogging platform in China, using a (CNN) approach. We propose a novel methodology that leverages the inherent structural dependencies among comments and users in the Weibo network to capture nuanced sentiment patterns. By representing Weibo comments as a graph, with users and comments as nodes and their interactions as edges, we exploit the relational information to enhance sentiment classification accuracy. Furthermore, we employ attention mechanisms to prioritize influential users and comments in the sentiment analysis process. Through extensive experiments on real Weibo datasets, our proposed CNN-based sentiment analysis framework demonstrates superior performance compared to traditional methods, achieving high accuracy in sentiment classification tasks. This research contributes to advancing sentiment analysis techniques in social media platforms and provides valuable insights for understanding public sentiment dynamics in online communities like Weibo.