An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa

Abstract

The aspect-category sentiment analysis can provide more and deeper information than the document-level sentiment analysis, because it aims to predict the sentiment polarities of different aspect categories in the same text. The main challenge of aspect-category sentiment analysis is that different aspect categories may present different polarities in the same text. Previous studies combine the Long Short-Term Memory (LSTM) and attention mechanism to predict the sentiment polarity of the given aspect category, but the LSTM-based methods are not really bidirectional text feature extraction methods. In this paper, we propose a multi-task aspect-category sentiment analysis model based on RoBERTa (Robustly Optimized BERT Pre-training Approach). Treating each aspect category as a subtask, we employ the RoBERTa based on deep bidirectional Transformer to extract features from both text and aspect tokens, and apply the cross-attention mechanism to guide the model to focus on the features most relevant to the given aspect category. According to the experimental results, the proposed model outperforms other models for comparison in aspect-category sentiment analysis. Furthermore, the influencing factors of our proposed model are also analyzed.