Filmymer: Movie Analysis: Sentiment Analysis Of Movie Review

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1. Introduction

The purpose of this document is to give a description about “FilmyMeter”. It will explain the purpose, scope, features, functionalities and user interface of FilmyMeter. This report includes what the system will do and the constraints under which it must operate.

1.1 Purpose
The Internet today contains a huge amount of textual data. This textual data can be classifying in two types, one is facts and other is opinion. While facts include objectives, opinion express the sentiment of people. In the movie industry, most of the movie’s success depend upon people working on the movie and the public response to the early trailers and promotion. This project aims at opinion mining and sentiment analysis of data related to movies.
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They have used neural networks to forecast and predict failure and success of a movie. They have used past records of the actor, actress, director, producers, music director, writer and also used marketing budget and time of release. Using confusion matrix, they have evaluated data and based on that they predicted movie’s success. [15]

Sentiment analysis of movie reviews:
Study paper given by V.K. Singh is an experiment work based on a heuristic for aspect level classification of sentiments. They have used SentiWordNet to compute sentiment of phrases and create sentiment profile of a movie. Based on their study they argue that their scheme produces more accurate results than the simple document-level sentiment profile. [16]

Polarity trend analysis of public sentiment on YouTube:
In their study Amar Krishna, Joseph Zambreno and Sandeep Krishnan investigate comments from YouTube videos and perform sentiment analysis on it based on several keywords. Their sentiment analysis was based on Naïve Bayes approach which identifies sentiment of 3 million comments. Through their study, they analyze how sentiments on social sites are related to real-world trends and events.
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Future Enhancement The following enhancements can be added to upgrade the system:

 We can add more data sources for sentiment analysis like Twitter, Facebook and another social medium where user post their opinions.
 We can add IMDB like sources which have more details about the movie and has a bigger user base.
 We can use computer with high processing power for natural language processing,

9. Conclusion

Thus, we can conclude that after successful analysis and planning “FilmyMeter” will fulfill all requirements and through this project’s successful development, the future work is to develop a public response prediction system for movies, which is a novel functionality.

Summary of Project Work It is a great achievement to successfully complete the project up till Design phase. The prior knowledge of software engineering has helped immensely in overcoming the various roadblocks that we faced during the project like:
- Finding the Definition.
- Getting the requirement.
- Doing the analysis on the requirement.
- Designing each place according to the requirement.
- Implementation and

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