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

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.