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Issue No.03 - March (2011 vol.23)
pp: 447-462
Stephen France , University of Wisconsin-Milwaukee, Milwaukee, WI
Zhu Zhang , University of Arizona, Tucson, AZ
Ahmed Abbasi , University of Wisconsin-Milwaukee, Milwaukee, WI
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.
Natural language processing, machine learning, text mining, subspace selection, affective computing.
Stephen France, Zhu Zhang, Ahmed Abbasi, "Selecting Attributes for Sentiment Classification Using Feature Relation Networks", IEEE Transactions on Knowledge & Data Engineering, vol.23, no. 3, pp. 447-462, March 2011, doi:10.1109/TKDE.2010.110
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