Fine-grained sentiment analysis using multidimensional feature fusion and GCN

Abstract

The demand for analysing sentiment information in social media data is increasing. However, current fine-grained sentiment analysis methods fail to consider both global and local semantic features simultaneously, leading to the oversight of grammatical information in sentences and an inability to address the issue of polysemy. To address these challenges, we propose a microblog fine-grained sentiment analysis model based on multidimensional feature fusion and graph convolutional neural networks (GCN). Built upon the ALBERT model, we utilize BiLSTM and capsule network models to extract global and local semantic features, thereby capturing bidirectional semantic dependencies and textual positional semantic information. Finally, we employ multi-head self-attention and GCN to select key features and sentence information, ensuring the integrity of fine-grained features. The experimental results indicate that the model outperforms several other models on fine-grained sentiment analysis datasets SMP2020-EWECT, NLPCC2013, NLPCC2014, and the binary classification dataset weibo_senti_100k, achieving accuracies of 80.64%, 67.19%, 71.37%, and 98.43%, respectively.

Keywords

• Fine-grained sentiment analysis
• ALBERT model
• multi-head self-attention
• graph convolutional network
• Multidimensional feature fusion