Targeted Sentiment Classification with Knowledge Powered Attention Network

Abstract

Targeted sentiment classification aims to identify the sentiment expressed towards some targets given context sentences, having great application value in social media, ecommerce platform and other fields. Most of the previous methods model context and target words with RNN and attention mechanism, which primarily do not use any external knowledge. In this paper, we utilize external knowledge from knowledge bases to reinforce the semantic representation of context and target. We propose a new model called Knowledge Powered Attention Network (KPAN), which uses the multi-head attention mechanism to represent target and context and to fuse with conceptual knowledge extracted from external knowledge bases. The experiments on three public datasets revealed that our proposed model outperforms the state-of-the-art methods, which signify the validity of our model.