DPG-LSTM: An Enhanced LSTM Framework for Sentiment Analysis in Social Media Text Based on Dependency Parsing and GCN

Abstract

Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% F1 scores on sentiment140.