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Feature Ranking in Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 94 - Number 13 |
| Year of Publication: 2014 |
| Authors: Maryam K. Jawadwala, Seema Kolkur |
10.5120/16407-6150
|
Maryam K. Jawadwala, Seema Kolkur . Feature Ranking in Sentiment Analysis. International Journal of Computer Applications. 94, 13 ( May 2014), 42-49. DOI=10.5120/16407-6150
Abstract
With the rapid expansion of e-commerce over the past 15 years, more products are sold on the Web. More and more people are buying products online. In order to enhance customer shopping experience, it has become a common practice for online merchants to enable their customers to write reviews on products that they have purchased. Some popular products can get hundreds of reviews or more at some large merchant sites. Manual analysis of customer opinions is only possible to a certain extent and very time-consuming due to the multitude of contributions. From the e-commerce perspective, receiving consumer's feedback can greatly improve its strategies in order to increase products of the sector. This research work will present feature wise sentiment analysis of customer review. The goal of feature level sentiment analysis is to produce a feature-based opinion summary of multiple reviews. With summaries of opinions and features of the product, people can make effective decisions in less time. Such mining can be helpful for competitive marketing. Feature extraction can be performed using two approaches. Rule-based algorithm and HAC algorithm. Feature ranking will be done using MAX opinionscore algorithm and opinion score obtained from SentiWordNet.
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