TY - GEN
T1 - Comparison of classification techniques for feature oriented sentiment analysis of product review data
AU - Pujari, Chetana
AU - Aiswarya,
AU - Shetty, Nisha P.
N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd. 2018.
PY - 2018
Y1 - 2018
N2 - 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 this work can be extended easily in all domains.
AB - 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 this work can be extended easily in all domains.
UR - http://www.scopus.com/inward/record.url?scp=85021248935&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021248935&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-3223-3_14
DO - 10.1007/978-981-10-3223-3_14
M3 - Conference contribution
AN - SCOPUS:85021248935
SN - 9789811032226
VL - 542
T3 - Advances in Intelligent Systems and Computing
SP - 149
EP - 158
BT - Data Engineering and Intelligent Computing - Proceedings of IC3T 2016
A2 - Janakiramaiah , B.
A2 - Bhateja, Vikrant
A2 - Raju, K. Srujan
A2 - Satapathy, Suresh Chandra
PB - Springer Verlag
T2 - 3rd International Conference on Computer and Communication Technologies, IC3T 2016
Y2 - 5 November 2016 through 6 November 2016
ER -
