An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews
DOI:
https://doi.org/10.4108/eai.13-7-2018.165670Keywords:
feature-based, sentiment analysis, positive sentiment, negative sentiment, polarity, product reviewsAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 EAI Endorsed Transactions on Scalable Information Systems
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.