Comparison of classification techniques for feature oriented sentiment analysis of product review data

Chetana Pujari, Aiswarya, Nisha P. Shetty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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 this work can be extended easily in all domains.

Original languageEnglish
Title of host publicationData Engineering and Intelligent Computing - Proceedings of IC3T 2016
PublisherSpringer Verlag
Pages149-158
Number of pages10
Volume542
ISBN (Print)9789811032226
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event3rd International Conference on Computer and Communication Technologies, IC3T 2016 - Vijayawada, India
Duration: 05-11-201606-11-2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume542
ISSN (Print)2194-5357

Conference

Conference3rd International Conference on Computer and Communication Technologies, IC3T 2016
CountryIndia
CityVijayawada
Period05-11-1606-11-16

Fingerprint

Customer satisfaction
Explosions
Support vector machines
Classifiers
Entropy

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Pujari, C., Aiswarya, & Shetty, N. P. (2018). Comparison of classification techniques for feature oriented sentiment analysis of product review data. In Data Engineering and Intelligent Computing - Proceedings of IC3T 2016 (Vol. 542, pp. 149-158). (Advances in Intelligent Systems and Computing; Vol. 542). Springer Verlag. https://doi.org/10.1007/978-981-10-3223-3_14
Pujari, Chetana ; Aiswarya ; Shetty, Nisha P. / Comparison of classification techniques for feature oriented sentiment analysis of product review data. Data Engineering and Intelligent Computing - Proceedings of IC3T 2016. Vol. 542 Springer Verlag, 2018. pp. 149-158 (Advances in Intelligent Systems and Computing).
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Pujari, C, Aiswarya & Shetty, NP 2018, Comparison of classification techniques for feature oriented sentiment analysis of product review data. in Data Engineering and Intelligent Computing - Proceedings of IC3T 2016. vol. 542, Advances in Intelligent Systems and Computing, vol. 542, Springer Verlag, pp. 149-158, 3rd International Conference on Computer and Communication Technologies, IC3T 2016, Vijayawada, India, 05-11-16. https://doi.org/10.1007/978-981-10-3223-3_14

Comparison of classification techniques for feature oriented sentiment analysis of product review data. / Pujari, Chetana; Aiswarya; Shetty, Nisha P.

Data Engineering and Intelligent Computing - Proceedings of IC3T 2016. Vol. 542 Springer Verlag, 2018. p. 149-158 (Advances in Intelligent Systems and Computing; Vol. 542).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Pujari C, Aiswarya, Shetty NP. Comparison of classification techniques for feature oriented sentiment analysis of product review data. In Data Engineering and Intelligent Computing - Proceedings of IC3T 2016. Vol. 542. Springer Verlag. 2018. p. 149-158. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-981-10-3223-3_14