Indian Journal of Science and Technology
Year: 2016, Volume: 9, Issue: 15, Pages: 1-6
A. Razia Sulthana* and Ramasamy Subburaj
School of Computing, SRM University, SRM Nagar, Potheri, Kattankulathur, Kancheepuram - 603203, Tamil Nadu, India; [email protected], [email protected]
*Author of Corresponding: A. Razia Sulthana School of Computing, SRM University, SRM Nagar, Potheri, Kattankulathur, Kancheepuram - 603203, Tamil Nadu, India; [email protected]
Background/Objectives: To provide a framework for improving the classification of customer reviews on products. Methods/Statistical Analysis: We propose an integrated framework for classifying the customer reviews based on the textual analysis with constraint-based association rules using ontology. It involves preprocessing the customer reviews including symbols and handling feature extraction. An improved K-Means algorithm with ontology is proposed to consolidate the reviews based on textual analysis method to handle reviews that represent at least one feature of the product. Findings: The empirical results reveal that the accuracy of the system increases with the use of ontology and modified K-Means algorithm, improving overall performance of the recommendation system. Combining preprocessing and ontology considerably improves the accuracy of classification of customer reviews. Applications/Improvements: The proposed approach can be used to recommend product based on users’ review.
Keywords: Classification, K-Means Clustering, Ontology, Preprocessing, Recommendations, Review
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