This document presents a project on sentiment analysis of movie reviews using machine learning techniques. The objectives are to detect sentiment polarity in online reviews and recommend movies. The proposed system would collect movie reviews, clean the data, categorize it using supervised machine learning, and determine sentiment. Related works apply sentiment analysis at the document, sentence, and sub-sentence levels using algorithms like bag-of-words. The project concludes that various machine learning techniques were studied to identify the polarity of movie reviews and a model was implemented using bag-of-words.

1. Sri Siddhartha Institute of Technology
Department of Computer Science and Engineering
Project work Presentation on
“SENTIMENTAL ANALYSIS OF MOVIE REVIEW USING
MACHINE LEARNING TECHNIQUES”
Student Name and USN
Priyadarshini R Charantimath 19CS075
Ruchitha P 19CS081
Under the Guidance of:
Priyanka D
Assistant Professor

2. Contents
Introduction
Objectives
Problem Statements and Proposed System
Related Works
Conclusion
References

3. Introduction
• Sentiment analysis is the process of using natural language processing, text
analysis, and statistics to analyze customer sentiment.
• Customer sentiment can be found in tweets, comments, reviews, or other places
where people mention your brand.
• Customer service agents often use sentiment or intent analysis to automatically
sort incoming user email into “urgent” or “not urgent” buckets based on the
sentiment of the email, proactively identifying frustrated users.

4. Objectives
 It is a relatively simplistic form of analytics that helps brands find key areas of
weakness (negative sentiments) and strengths (positive sentiments).
 To detect the polarity in the thoughts and opinions of all the users that access
social media.

5. Problem Statement and Proposed System
• Movie recommendation is a comprehensive and complicated task which
involves various tastes of users, various genres of movies, and so forth.
Therefore, lots of techniques for recommendation have been proposed to solve
the problems.
• In situations where the speed of classification is an issue, one can use the
linear classifier as fastest classifier if the available vector is sparse.
 The systematic procedures for the proposed methodology
 Collecting Movie Review
 Cleaning the Data Sets Movie review
 Data Categorization Supervised machine learning techniques

6. Related Works and Technologies
• Sentiment Analysis is applied for the following operations:
 Find and extract the opinionated data (aka sentiment data) on a specific platform
(customer support, reviews, etc.)
 Determine its polarity (positive or negative)
 Define the subject matter (what is being talked about in general and specifically)
 Identify the opinion holder (on its own and in correlation with the existing audience
segments)

7. Related Works and Technologies
• sentiment analysis algorithm can be used at the following
scopes:
 Document-level - for the entire text.
 Sentence-level - obtains the sentiment of a single sentence.
 Sub-sentence level - obtains the sentiment of sub-expressions within a
sentence.

8. Conclusion
• In this project work, various techniques were studied to identify
the polarity of the movie reviews.
• The work explains the implementation of the machine learning
technique using bag-of-words technique.

9. References
[1] A Sentiment-Enhanced Hybrid Recommender System for Movie
recommendation: A Big Data Analytics Framework, Yibo Wang, Mingming Wang,
and Wei Xu, 22 March 2018.
[2] Sentiment Analysis of Movie Review Using Supervised Machine Learning
Techniques, Gurshobit Singh Brar, Asst. Prof. Ankit Sharma
[3] Movies Reviews Sentiment Analysis and Classification, Mais Yasen, Sara
Tedmori, Department of Computer Science, Princess Sumaya University for
Technology Amman, Jordan

10. Thank You