Employing relation extraction technology to jointly recognize aspect-polarity pairs in a text

Abstract:

Aspect-based sentiment analysis aims to identify the aspects mentioned in sentences and their sentiment polarity, which is an important task in fine-grained sentiment analysis. The existing studies use sequence labeling or span-based classification methods, having their own defects such as polarity inconsistency resulted from separately tagging tokens in the former and the heterogeneous categorization in the latter where aspect-related and polarity-related labels are mixed. At the same time, the existing methods ignore the correlation between aspect-polarity pairs in sentences. In order to remedy the above defects, inspiring from the recent advancements in relation extraction, we propose to generate aspect-polarity pairs directly from a text with relation extraction technology, regarding aspect-pairs as unary relations where aspects are entities and the corresponding polarities are relations and utilize sequence decoding to capture the correlation between aspect-polar pairs. The experiments performed on three benchmark datasets demonstrate that our model outperforms the existing state-of-the-art approaches.