Online Product Opinion and Ranking System

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 test beds 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.

Existing System

The existing feature selection methods do not adequately address attribute relevance and redundancy issues, which are critical for text sentiment analysis.

Proposed System

We propose the use of a rich set of n-gram features spanning many fixed and variable n-gram categories. We couple the extended feature set with a feature selection method capable of efficiently identifying an enhanced subset of n-grams for opinion classification. The proposed Feature Relation Network is a rule-based multivariate n-gram feature selection technique that efficiently removes redundant or less useful n-grams, allowing for more effective n-gram feature sets. Experimental results reveal that the extended feature set and proposed feature selection method can improve opinion classification performance over existing selection methods.

Architecture