sis 18: e26

Research Article

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

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  • @ARTICLE{10.4108/eai.13-7-2018.165670,
        author={A. K. Yadav and D. Yadav and A. Jain},
        title={An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={7},
        keywords={feature-based, sentiment analysis, positive sentiment, negative sentiment, polarity, product reviews},
        doi={10.4108/eai.13-7-2018.165670}
    }
    
  • A. K. Yadav
    D. Yadav
    A. Jain
    Year: 2020
    An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.165670
A. K. Yadav1, D. Yadav1, A. Jain2,*
  • 1: CSE, National Institute of Technology, Hamirpur, Himachal Pradesh, India
  • 2: CSE, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India
*Contact email: ajain.jiit@gmail.com

Abstract

In today’s society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users’ reactions towards the products that are available in the market at an early stage. It thus intimates users’ perception and charts out a path that is beneficial for the market to grow as a whole. Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the detailed components based products and their associated features, a novel method is desired to meet these criteria. So far, no such method is explored that analyses the product’s components and their features simultaneously, on the basis of sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation of double negative sentences, detecting questions and emotions from the review sentences are measured which increases efficiency of the FBA method. The comparison of product’s components reviews is done with other existing algorithmsTF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.