Comparison of classification techniques for Feature Oriented Sentiment Analysis of Product Review Data

Pujari, Chetana and Aiswarya, . and Shetty, Nisha P (2016) Comparison of classification techniques for Feature Oriented Sentiment Analysis of Product Review Data. In: IC3T, 05/11/2016, Vijaywada.

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Abstract

With the rapid increase in popularity of e-commerce services over the years, all varieties of products are sold online today. Posting online reviews has become a common means for people to express their impressions on any product, while serving as a recommendation for others. To enhance customer satisfaction and buying experience, often the sellers provide a platform for the customers to express their views. Due to the explosion of these opinion rich sites where numerous opinions about a product are expressed, a potential customer finds it difficult to read all the reviews and form an intelligent opinion about the product. In this research, a new framework comprising of the inbuilt packages of python is designed which mines many customers’ opinions about a product and groups them accordingly based on their sentiments, which aids the potential buyers to form a capitalized view on the product. Here classification of the reviews is done using three different classification algorithms i.e. Naïve Bayes Algorithm, Maximum Entropy Classifier and SVM (Support Vector Machine), and their performance is compared. The methodology showcased in our work can be extended easily in all domains.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Feature based Opinion Mining, Sentiment Analysis,Tokenization, Bigram Collocation, Classification, nltk, Anaconda
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 06 Jan 2017 09:33
Last Modified: 06 Jan 2017 09:33
URI: http://eprints.manipal.edu/id/eprint/147888

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