International Journal of Knowledge Based Computer System

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Applying MCDM Techniques for Ranking Products Based on Online Customer Feedback


Affiliations
1 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
2 Department of Computer Science and Engineering, Vickram College of Engineering, Enathi, Tamil Nadu, India
     

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Text analytics is to distil out structured information from unstructured or semi-structured text. User feedback analysis or sentiment analysis on products enables to highlight the best and worst of features and recommend the product to new buyers. The model extracts the positive and negative comments and identifies the emotions in the piece of text or n-way analysis and classification like very-positive, positive, neutral, negative or very-negative. Natural Language Processing (NLP) tools play vital role in classifying the sentiment polarity of sentences while data analytics has the role in recommendation of the product. In this paper, we propose a recommender system model to rank the products based on the feedback given by the users. Features, the topics of interest, are identified from the set of review text. Sentiments are detected from each review and thus senti-score is calculated for each feature of the product. We use the Analytic Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which are Multi-Criteria Decision Making techniques to rank a set of products. This method provides a logical framework to determine the benefits of each product based on the features and thus the products are ranked.

Keywords

Sentiment Analysis, User Feedback Analysis, Multi-Criteria Decision Making, Technique for Order Preference by Similarity to Ideal Solution, Analytic Hierarchy Process.
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Abstract Views: 356

PDF Views: 2




  • Applying MCDM Techniques for Ranking Products Based on Online Customer Feedback

Abstract Views: 356  |  PDF Views: 2

Authors

J. Santhana Preethi
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
A. M. Abirami
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
A. Askarunisa
Department of Computer Science and Engineering, Vickram College of Engineering, Enathi, Tamil Nadu, India
G. Sathya Priya
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India
E. Sankaragomathy
Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

Abstract


Text analytics is to distil out structured information from unstructured or semi-structured text. User feedback analysis or sentiment analysis on products enables to highlight the best and worst of features and recommend the product to new buyers. The model extracts the positive and negative comments and identifies the emotions in the piece of text or n-way analysis and classification like very-positive, positive, neutral, negative or very-negative. Natural Language Processing (NLP) tools play vital role in classifying the sentiment polarity of sentences while data analytics has the role in recommendation of the product. In this paper, we propose a recommender system model to rank the products based on the feedback given by the users. Features, the topics of interest, are identified from the set of review text. Sentiments are detected from each review and thus senti-score is calculated for each feature of the product. We use the Analytic Hierarchy Process and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), which are Multi-Criteria Decision Making techniques to rank a set of products. This method provides a logical framework to determine the benefits of each product based on the features and thus the products are ranked.

Keywords


Sentiment Analysis, User Feedback Analysis, Multi-Criteria Decision Making, Technique for Order Preference by Similarity to Ideal Solution, Analytic Hierarchy Process.

References