Sentence compression for target-polarityword collocation extraction

Abstract

Target-polarity word (T-P) collocation extraction, a basic sentiment analysis task, relies primarily on syntactic features to identify the relationships between targets and polarity words. A major problem of current research is that this task focuses on customer reviews, which are natural or spontaneous, thus posing a challenge to syntactic parsers. We address this problem by proposing a framework of adding a sentiment sentence compression (Sent Comp) step before performing T-P collocation extraction. Sent Comp seeks to remove the unnecessary information for sentiment analysis, thereby compressing a complicated sentence into one that is shorter and easier to parse. We apply a discriminative conditional random field model, with some special sentimentrelated features, in order to automatically compress sentiment sentences. Experiments show that Sent Comp significantly improves the performance of T-P collocation extraction.