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

A. Razia Sulthana; Ramasamy Subburaj

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

机译：基于改进的本体的K-均值聚类方法用于顾客评论分类

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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.

机译：背景/目的：提供一个框架，以改善对产品的客户评论的分类。方法/统计分析：我们提出了一个集成的框架，用于基于文本分析和使用本体的基于约束的关联规则对客户评论进行分类。它涉及预处理客户评论，包括符号和处理特征提取。提出了一种带有本体的改进的K-Means算法，以基于文本分析方法对评论进行合并，以处理代表产品至少一个特征的评论。结果：实证结果表明，该系统的准确性随着使用本体和改进的K-Means算法的使用而提高，从而提高了推荐系统的整体性能。将预处理和本体结合起来可以大大提高客户评论分类的准确性。应用程序/改进：所建议的方法可用于根据用户的评论推荐产品。

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《Indian Journal of Science and Technology》 |2016年第15期|共页
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A. Razia Sulthana; Ramasamy Subburaj;
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An Improvised Ontology based K-Means Clustering Approach for Classification of Customer Reviews

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