VOICE OVER SENTIMENT ANALYSIS FOR MOVIE REVIEW SYSTEM

INFORMATION TECHNOLOGY

To comprehend the sentiments portrayed in text data, sentiment analysis is essential. Because Tamil is a colloquial language and contains many idiomatic terms, proper sentiment analysis of Tamil movie reviews is difficult. This study suggests a Python-based voice-over sentiment analysis system for Tamil movie reviews. The technology uses cuttingedge natural language processing (NLP) and machine learning techniques to assess the voice-over's text and audio components. The goal is to offer a thorough sentiment analysis of informal Tamil movie reviews that takes into account both language and emotional cues. The practice of identifying the sentiment or emotion expressed in a text is known as sentiment analysis. Sentiment analysis has grown in importance as a tool for analyzing the reactions of audiences to films as moviegoing has gotten more and more popular and user-generated material has increased on social media platforms. This abstract outlines a thorough method for utilizing Python to conduct sentiment analysis on informal Tamil movie reviews. The purpose of this project is to create a system for categorizing Tamil movie reviews as favorable, bad, or neutral. The system will use natural language processing (NLP) and machine learning methods to analyze the sentiment included in the text. Domain-specific lexicons and sentiment dictionaries will be developed and used to increase the precision of the sentiment analysis. Specific terms and idioms connected to Tamil movies will be captured by these lexicons. The proposed Tamil movie review system will offer insightful information on how the audience feels about movies. Filmmakers, distributors, and marketers can use it to evaluate the performance and reception of their films. It can also assist movie lovers in making intelligent choices regarding the films they will view. Tamil movie reviews will make up a sizable portion of the dataset utilized for training and evaluation, ensuring the system's accuracy. The advantages and uses of the suggested system are also highlighted, including its ability to help viewers make educated selections, offer insightful information to distributors and makers of motion pictures, and improve tailored movie suggestions. The report also highlights future work on adding idiomatic terms to the sentiment lexicon and using appropriate datasets for analysis. Overall, this paper gives a thorough framework for voice-over sentiment analysis in Tamil movie reviews, demonstrating the power of Python and NLP tools for studying spoken languages. The technology intends to expand sentiment analysis methodologies and improve comprehension of audience attitudes toward Tamil movies.

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