Abstract
A major concern when incorporating large sets of diverse n-gram features for sentiment classification is the presence of noisy, irrelevant, and redundant attributes. These concerns can often make it difficult to harness the augmented discriminatory potential of extended feature sets. We propose a rule-based multivariate text feature selection method called Feature Relation Network (FRN) that considers semantic information and also leverages the syntactic relationships between n-gram features. FRN is intended to efficiently enable the inclusion of extended sets of heterogeneous n-gram features for enhanced sentiment classification. Experiments were conducted on three online review testbeds in comparison with methods used in prior sentiment classification research. FRN outperformed the comparison univariate, multivariate, and hybrid feature selection methods; it was able to select attributes resulting in significantly better classification accuracy irrespective of the feature subset sizes. Furthermore, by incorporating syntactic information about n-gram relations, FRN is able to select features in a more computationally efficient manner than many multivariate and hybrid techniques.
Original language | English (US) |
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Article number | 5510238 |
Pages (from-to) | 447-462 |
Number of pages | 16 |
Journal | IEEE Transactions on Knowledge and Data Engineering |
Volume | 23 |
Issue number | 3 |
DOIs | |
State | Published - 2011 |
Keywords
- Natural language processing
- affective computing
- machine learning
- subspace selection
- text mining
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics