Sentiment Lexicon Expansion Based on Neural PU Learning, Double Dictionary Lookup, and Polarity Association

Abstract

Although many sentiment lexicons in different languages exist, most are not comprehensive. In a recent sentiment analysis application, we used a large Chinese sentiment lexicon and found that it missed a large number of sentiment words used in social media. This prompted us to make a new attempt to study sentiment lexicon expansion. This paper first formulates the problem as a PU learning problem. It then proposes a new PU learning method suitable for the problem based on a neural network. The results are further enhanced with a new dictionary lookup technique and a novel polarity classification algorithm. Experimental results show that the proposed approach greatly outperforms baseline methods.