关键词: 特定目标情感分析, 交互注意力网络, 注意力机制

Abstract:

Target-based sentiment analysis aims to identify the sentiment polarity of specific target in its context. In recent years, more and more researchers use various methods based on neural networks to solve target-based sentiment analysis, and achieve success. However, most target-related models only focus on the impact of target on context modeling, but ignore the role of context in target modeling. To address this problem, this paper proposes an interactive attention networks named LT-T-TR for target-based sentiment analysis, which divides a review into three parts：left context with target phrase, target phrase, and right context with target phrase. The interaction between target phrase and left/right context is utilized by attention mechanism to learn the representations of target phrase and left/right context separately. As a result, the most important words in target phrase or in left/right context are captured, and the results on laptop and restaurant datasets demonstrate the effectiveness of the model.

Key words: target-based sentiment analysis, interactive attention networks, attention mechanism