An Improvised Ideology based K-Means Clustering Approach for Classification of Customer Reviews
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
How to Cite?: Kinnari Mishra, "An Improvised Ideology based K-Means Clustering Approach for Classification of Customer Reviews", Volume 13 Issue 4, April 2024, International Journal of Science and Research (IJSR), Pages: 1330-1334, https://www.ijsr.net/getabstract.php?paperid=SR24212214435, DOI: https://dx.doi.org/10.21275/SR24212214435