Exploring Scope Detection for Aspect-Based Sentiment Analysis
Xiaotong Jiang, Peiwen You, Chen Chen, Zhongqing Wang, Guodong Zhou
Abstract
Aspect-based sentiment analysis (ABSA) aims to extract the aspect terms from review text, and to predict the polarity towards the aspect term. Although neural models have achieved competitive results, there are still many challenges in this task. Firstly, there is irrelevant and noise information in the review text, and the offsets of aspect term boundary are hard to decide. In addition, sentiment is usually either expressed implicitly or shifted due to the occurrence of negation and rhetorical words. To tackle the above limitations, we propose a scope detection model to distinguish whether the words from the review text are relevant with the aspect term, and to filter irrelevant and noise information. In addition, we investigate a biaffine-based model to constrain the scope detection process of aspect term extraction. We further generate a simplified clause based on the scope of aspect term, and predict the polarity based on the simplified clause. Empirical studies show the effectiveness of our proposed model over several strong baselines. These also justify the importance of scope detection for aspect-based sentiment analysis.