Aspect Sentiment Classification Towards Question-Answering with
Reinforced Bidirectional Attention Network
Abstract
In the literature, existing studies on aspect sentiment classification (ASC) focus on individual non-interactive reviews. This paper extends the research to interactive reviews and
proposes a new research task, namely Aspect
Sentiment Classification towards QuestionAnswering (ASC-QA), for real-world applications. This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews. In particular, a
high-quality annotated corpus is constructed
for ASC-QA to facilitate corresponding research. On this basis, a Reinforced Bidirectional Attention Network (RBAN) approach is
proposed to address two inherent challenges
in ASC-QA, i.e., semantic matching between
question and answer, and data noise. Experimental results demonstrate the great advantage
of the proposed approach to ASC-QA against
several state-of-the-art baselines