Short text sentiment analysis combining sentiment lexicon and graph convolutional networks

Abstract

In today's era of rapid development in information technology, short-text data has surged on various social networking platforms. How to quickly and accurately analyze people's emotional tendencies from these vast and complex data is a highly challenging task in the field of short-text data analysis. This paper proposes a short-text sentiment analysis framework that integrates a sentiment lexicon and graph convolutional neural networks (GCN). The framework utilizes the sentiment dictionary to enhance sentiment recognition and employs GCN to process complex data structures, learning the emotional features of short texts, and ultimately achieving short-text sentiment classification. To verify the effectiveness of the model, we conducted validation on public datasets. The experimental results show that this model significantly improves classification accuracy and recall rate compared to traditional single models.