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Feature Relevance Analysis in On-Line Marketing to Improve Productivity

P. Dhana Lakshmi*, Kasarapu Ramani**, B. Eswara Reddy***

* Assistant Professor, Sree Vidyanikethan Engineering College, Tirupati, India.

** Professor and Head, Sree Vidyanikethan Engineering College, Tirupati, India.

*** Professor, Jawaharlal Nehru Technological University, Anantapuramu, India.

Periodicity:January - March'2015
DOI : https://doi.org/10.26634/jse.9.3.3467

Abstract

Opinion mining applications play a major role in identifying user perspectives. To extract useful information from huge volume of web resources, discussion forums, review sites and blogs is becoming a challenge. Majority of opinion mining approaches for feature extraction is based on static keywords appearing in single product review documents which may omit even relevant reviews. An automated opinion mining mechanism to produce summary of opinions based on a set of product reviews and multiple product features is needed. In this paper a technique for product feature relevance analysis using text mining concepts is proposed. The experiment results on Amazon mobile and office products review data shows the improvement in accuracy and efficiency of the proposed system over existing techniques.

Keywords

Text Mining, Opinion Mining, Web Mining, Sentimental Analysis, Feature List, Summarization of Reviews.

How to Cite this Article?

Lakshmi, P. D., Ramani, K., and Reddy, E. B. (2015). Feature Relevance Analysis in On-Line Marketing to Improve Productivity. i-manager’s Journal on Software Engineering, 9(3), 1-10. https://doi.org/10.26634/jse.9.3.3467

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