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Analysis Study Research Paper | Computer Science and Information Technology | India | Volume 13 Issue 4, April 2024 | Rating: 4.7 / 10

An Improvised Ideology based K-Means Clustering Approach for Classification of Customer Reviews

Kinnari Mishra

Abstract: 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

Edition: Volume 13 Issue 4, April 2024,

Pages: 1330 - 1334