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Introduction

In recent years, the exponential growth of online movie review platforms has provided moviegoers with a plethora of opinions and critiques on the latest films. Analyzing these reviews manually can be a daunting task due to the sheer volume of data available. This is where sentiment analysis using natural language processing (NLP) comes into play. Sentiment analysis is a branch of NLP that focuses on extracting subjective information, such as emotions and opinions, from text data. By applying sentiment analysis to movie reviews, researchers can gain insights into the public’s perception of a film, helping filmmakers and producers understand audience reactions.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Sentiment Analysis
2.2 NLP Techniques for Sentiment Analysis
2.3 Sentiment Analysis in Movie Reviews
2.4 Existing Sentiment Analysis Tools
2.5 Challenges in Sentiment Analysis
2.6 Applications of Sentiment Analysis in the Film Industry
2.7 Sentiment Analysis Evaluation Metrics
2.8 Sentiment Analysis Datasets
2.9 Trends in Sentiment Analysis Research
2.10 Summary of Literature Review

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Sentiment Analysis Model Selection
3.6 Model Evaluation
3.7 Experimental Setup
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Overview of Dataset
4.2 Performance of Sentiment Analysis Models
4.3 Sentiment Analysis Results
4.4 Comparison with Existing Tools
4.5 Implications of Findings
4.6 Limitations of the Study
4.7 Future Research Directions

Chapter 5: Conclusion
5.1 Summary of Key Findings
5.2 Contributions of the Study
5.3 Practical Implications
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

Sentiment analysis of movie reviews using natural language processing is a comprehensive study that aims to analyze the sentiment expressed in online movie reviews. Chapter one provides an introduction to the research topic, including background information, problem statement, objectives, and limitations of the study. The significance of the study and the structure of the thesis are also discussed in this chapter.

Chapter two presents a thorough literature review on sentiment analysis, NLP techniques, sentiment analysis in movie reviews, existing tools, challenges, applications in the film industry, evaluation metrics, datasets, and research trends. This chapter lays the foundation for the research methodology in chapter three, which covers research design, data collection, preprocessing, feature extraction, model selection, evaluation, experimental setup, and ethical considerations.

Chapter four delves into the discussion of findings, including an overview of the dataset, model performance, sentiment analysis results, comparison with existing tools, implications, limitations, and future research directions. Finally, chapter five concludes the thesis by summarizing key findings, discussing contributions, practical implications, recommendations for future research, and concluding remarks on the project.

Overall, this thesis aims to contribute to the growing field of sentiment analysis by providing insights into the sentiment of movie reviews using NLP techniques. By analyzing the public’s perception of films, this study can help filmmakers and producers understand audience reactions and make informed decisions in the film industry.

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