Unsupervised Sentiment-Bearing Feature Selection for Document-Level Sentiment Classification

Yan LI  Zhen QIN  Weiran XU  Heng JI  Jun GUO  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.12   pp.2805-2813
Publication Date: 2013/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2805
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
feature selection,  sentiment discriminant analysis,  sentiment strength calculation,  sentiment classification,  

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Text sentiment classification aims to automatically classify subjective documents into different sentiment-oriented categories (e.g. positive/negative). Given the high dimensionality of features describing documents, how to effectively select the most useful ones, referred to as sentiment-bearing features, with a lack of sentiment class labels is crucial for improving the classification performance. This paper proposes an unsupervised sentiment-bearing feature selection method (USFS), which incorporates sentiment discriminant analysis (SDA) into sentiment strength calculation (SSC). SDA applies traditional linear discriminant analysis (LDA) in an unsupervised manner without losing local sentiment information between documents. We use SSC to calculate the overall sentiment strength for each single feature based on its affinities with some sentiment priors. Experiments, performed using benchmark movie reviews, demonstrated the superior performance of USFS.