SentReP: Sentiment Classification of Movie Reviews using Efficient Repetitive Pre-Processing

Author

Manek, Asha S. ; Pallavi, R.P. ; Bhat, Veena H. ; Shenoy, P. Deepa ; Mohan, M. Chandra ; Venugopal, K.R. ; Patnaik, L.M.

Author_Institution

Dept. of Comput. Sci. & Eng., Jawaharlal Nehru Technol. Univ., Hyderabad, India

fYear

2013

fDate

22-25 Oct. 2013

Firstpage

1

Lastpage

5

Abstract

Opinions are highly essential for decision making and popular among the internet users. People with malicious intentions tend to give fake reviews to encourage or degrade the products. Reviewing movies is gaining popularity among web users, at the same time cannot be trusted. In this work, we propose a model Sentiment Classification of Movie Reviews using Efficient Repetitive Pre-processing (SentReP) that is based on tested parameters and a focused pre-processing technique to classify opinions. Working on the Cornell Movie review data set, this work significantly proves the accuracy and effectiveness of SentReP across different volumes of data and when compared to other different prevailing approaches. Overall this approach is very efficient in analyzing sentiments of movie reviews.

Keywords

Internet; decision making; humanities; pattern classification; reviews; Cornell Movie review data set; Internet users; SentReP; Web users; decision making; fake reviews; malicious intentions; repetitive preprocessing technique; sentiment classification of movie review model; Accuracy; Analytical models; Data mining; Motion pictures; Support vector machines; Training; Unsolicited electronic mail; Fake Reviews; Movie Review Data set; Opinion Mining; Pre-Processing Techniques; SentReP;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2013 - 2013 IEEE Region 10 Conference (31194)

Conference_Location

Xi´an

ISSN

2159-3442

Print_ISBN

978-1-4799-2825-5

Type

conf

DOI

10.1109/TENCON.2013.6718944

Filename

6718944