A Multi-Task Learning Approach to Improve Sentiment Analysis with Explicit Recommendation

Abstract

When expressing sentiment towards products, customers often explicitly indicate their recommendation status. Nevertheless, most existing literature focuses on sentiment analysis but neglects the rich correlation information that may be brought by explicit recommendation classification. We argue that the two tasks are correlated and hence, the knowledge in explicit recommendation classification can also be beneficial to sentiment analysis. Consequently, in this paper, a novel bidirectional encoder representations from transformers (BERT)-enhanced multi-task learning (BeMTL) approach is proposed to improve sentiment analysis with explicit recommendation classification. Specifically, the proposed MTL approach takes contextualized word embeddings produced by the pre-trained BERT-based embedding layer. Then, it learns the sentence contextual features shared between both tasks with a convolutional multi-head attention neural network. To fully exploit the correlation information between sentiment analysis and explicit recommendation classification tasks, a novel inter-task matching layer (IML) is designed to match their representations. In nutshell, our study reveals the potential of multi-task learning models on such types of problems, and experimental results on two Amazon datasets show that our approach outperforms the state-of-the-art baseline approaches for sentiment analysis.