Recent solutions for sentiment analysis have relied on feature selection methods ranging from lexicon-based approaches where the set of features are generated by humans, to approaches that use general statistical measures where features are selected solely on empirical evidence. The advantage of statistical approaches is that they are fully automatic, however, they often fail to separate features that carry sentiment from those that do not. In this paper we propose a set of new feature selection schemes that use a Content and Syntax model to automatically learn a set of features in a review document by separating the entities that are being reviewed from the subjective expressions that describe those entities in terms of polarities. By focusing only on the subjective expressions and ignoring the entities, we can choose more salient features for document-level sentiment analysis. The results obtained from using these features in a maximum entropy classifier are competitive with the state-of-the-art machine learning approaches.
REF approached the task of feature selection for sentiment analysis by using a Content and Syntax model, HMM-LDA, to separate the entities in a review document from the potentially sentiment carrying modifiers.
Feature Selection for Sentiment Analysis Based on Content and Syntax Models
Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2.011)
Computer Science