Sentiment Analysis Based on Background Knowledge Attention

Abstract

Sentiment analysis, which is a fundamental research in the field of natural language processing and artificial intelligence field, has received much attention these years because of its practical applicability and the challenges. However, existing methods only focus on local text information and ignore the background knowledge (such as the director of a movie, the producer of a product). In this paper, we propose a novel LSTM with Background Knowledge Attention Model (LSTM-BKAM) for sentiment analysis. Our model incorporates background knowledge based attentions over different semantic parts of a sentence. The experiment results show that our model achieves state-of-the-art, and substantially better than other approaches.