Sentiment analysis via integrating distributed representations of variable-length word sequence

Abstract

Sentiment analysis aims to identify the overall emotional polarity of a given text. It is a nontrivial task to perform sentiment analysis as sentiment information is crucial in many natural language processing applications. Previous n-gram features is derived from a bag-of-n-gram model which is insensitive to the order of the n-gram. To address this problem, we integrates distributed semantic features of word sequence, with fixed-size independent of the length of the word sequence. We also learn distributed semantic features of part-of-speech (POS) sequence as additional syntax-related clues to sentiment analysis. Our semantic features are able to capture both local contexts and global contexts automatically without involving comprehensive task-specific feature engineering. We validate the effectiveness of the method on our constructed sentiment dataset. Experiment results show that our method are able to improve the quality of sentiment analysis when comparing with several competitive baselines.