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

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.