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Introduction

In recent years, social media platforms have become a prominent source of information for businesses, marketers, and consumers. With the rise of social media, companies are now able to interact with their customers in real-time, gaining valuable insights into their preferences and opinions. One of the key challenges for businesses is to effectively analyze the sentiment of social media posts related to product launches in order to understand consumer reactions and improve their marketing strategies.

Sentiment analysis, also known as opinion mining, is a research field that deals with the computational analysis of opinions, sentiments, and emotions expressed in text. By using text mining techniques and deep learning algorithms, researchers can automatically extract and classify sentiments from social media posts, allowing businesses to gain valuable insights into consumer perceptions and attitudes towards their products.

This thesis aims to explore the application of sentiment analysis for product launch analysis using text mining and deep learning techniques. By analyzing social media posts related to product launches, this study seeks to uncover patterns and trends in consumer sentiments, identify key factors influencing consumer perceptions, and provide recommendations for marketers to improve their product launch strategies.

Table of Contents

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 Text Mining Techniques
2.3 Deep Learning Algorithms
2.4 Product Launch Analysis
2.5 Social Media Marketing
2.6 Consumer Behavior
2.7 Sentiment Analysis Tools
2.8 Challenges in Sentiment Analysis
2.9 Previous Studies on Social Media Sentiment Analysis
2.10 Gaps in Literature

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Sentiment Analysis Model
3.5 Evaluation Metrics
3.6 Data Analysis Techniques
3.7 Validity and Reliability
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Overview of Data Analysis
4.2 Patterns and Trends in Consumer Sentiments
4.3 Factors Influencing Consumer Perceptions
4.4 Implications for Marketers
4.5 Recommendations for Product Launch Strategies

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions to Research
5.4 Practical Implications
5.5 Future Research Directions

This thesis will provide valuable insights into the application of sentiment analysis for product launch analysis using text mining and deep learning techniques. By leveraging the power of social media data, businesses can enhance their understanding of consumer sentiments, optimize their marketing strategies, and ultimately improve the success of their product launches.

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