Improving Implicit Sentiments Analysis via Explanations of Multiple Perspectives

Abstract

Implicit sentiment expressions convey emotions indirectly, through context or factual statements, rather than explicit opinion words. Recent research on implicit sentiment analysis overlooks the fact that various individuals can interpret the same implicit expressions in different manners and experience different sentiments. Additionally, most previous research mainly focuses on implicit sentiment classification, neglecting the reasons behind the results. It hinders the deep understanding of the complexities involved in human emotions and limits the application of sentiment analysis. In this work, we introduce a new task, Abisa-Ex, which aims at both sentiment classification and explanation generation. We re-labeled the previous aspect-based implicit sentiment analysis dataset, incorporating new (sentiment, explanation) pair labels provided by various annotators. Based on the new dataset, we design frameworks to allow models to learn to predict sentiments from different perspectives and provide reasonable explanations jointly. Notably, our work shows that learning explanations from various viewpoints not only allows the model to generate the logical process behind sentiment analysis, but also significantly improves the model’s sentiment classification performance.