Sentiment Analysis for Hindi Movie Reviews

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Sentiment Analysis for Hindi Movie Reviews

Prerna Katte

Dept. of Information Technology Vidyalankar Institute of Technology

Mumbai, India prerna.katte@vit.edu.in

Sandeep Dhenaki

Dept. of Information Technology Vidyalankar Institute of Technology

Mumbai, India sandeep.dhenaki@vit.edu.in

Sanika Gaikwad

Dept. of Information Technology Vidyalankar Institute of Technology

Mumbai, India sanika.gaikwad@vit.edu.in

Dr. Vipul Dalal

Dept. of Information Technology Vidyalankar Institute of Technology Mumbai, India

vipul.dalal@vit.edu.in

Abstract — This research paper presents the development and implementation of an advanced Sentiment Analysis system for Hindi movie reviews, employing diverse methodologies to enhance accuracy and applicability. The study encompasses the utilization of resource-based semantic analysis, in-language semantic analysis, and machine translation-based semantic analysis to effectively classify sentiments of Hindi movie reviews. The dataset, comprising 1000 movie reviews, is meticulously curated from diverse sources, including IIT-Bombay and Jagaran.com, providing a robust foundation for model training and evaluation. The project employs various classification techniques, such as decision trees and deep belief networks, offering a comprehensive understanding of the nuances in sentiment classification. By integrating multiple approaches, the research aims to provide an accurate and reliable sentiment analysis solution for Hindi movie reviews, contributing to the broader field of natural language processing and sentiment analysis. The paper emphasizes the significance of each approach, their respective strengths, and the potential for future improvements in sentiment analysis on multilingual datasets.

Keywords—Sentiment Analysis, Deep Belief Network, Machine Learning, HindiSentiWordnet, Tensorflow.

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