Indian Journal of Science and Technology

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off


Background/Objective: Online shopping encompasses large variety of products and reviews which gives rich and valuable source of information for both enterprise and user. The review available on the internet is often disorganized that makes the user a difficulty in navigating the information and gaining knowledge. The aim of this study is to predict the users' opinion of a product based on their online reviews. Methods: We propose a technique called semantic orientation, which automatically finds the frequently used terms for an aspect of a product from online customer reviews and other important context considered here is a dynamic dataset. Firstly the product aspect is identified, and then sentiment classification is done. Additionally, other techniques like stop words removal, context based mining and stemming is employed. Findings: It provides an efficient way of predicting the user's opinion and thereby suggesting them. Applications/Improvements: The proposed system has been tested in various products and is analysed. This system can be implemented in real world application like predicting opinions of the user. Hence improving the user's accessibility.

Keywords

Aspect Ranking, Context Mining, Product Aspect, Sentiment Classification, Stemming, Stop Words.
User