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Introduction:

In the era of digitalization, customer reviews have become a valuable source of information for businesses looking to expand their product categories. Sentiment analysis, also known as opinion mining, is a powerful tool that allows companies to extract insights from customer feedback and make data-driven decisions. This study aims to explore the application of sentiment analysis using text mining and machine learning techniques to analyze customer reviews for product category expansion.

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 Two: Literature Review
2.1 Overview of sentiment analysis
2.2 Text mining techniques
2.3 Machine learning algorithms
2.4 Applications of sentiment analysis in business
2.5 Customer review analysis for product expansion
2.6 Challenges and limitations of sentiment analysis
2.7 Recent research studies in sentiment analysis
2.8 Integration of text mining and machine learning in sentiment analysis
2.9 Importance of customer feedback in decision-making
2.10 Future trends in sentiment analysis

Chapter Three: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Sentiment analysis techniques
3.5 Machine learning models selection
3.6 Evaluation metrics
3.7 Validation methods
3.8 Ethical considerations

Chapter Four: Discussion of Findings
4.1 Analysis of customer reviews for product category expansion
4.2 Sentiment polarity detection
4.3 Feature extraction and sentiment classification
4.4 Model performance evaluation
4.5 Comparison of machine learning algorithms
4.6 Interpretation of results
4.7 Implications for businesses
4.8 Recommendations for future research

Chapter Five: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Limitations of the study
5.5 Suggestions for future research

Thesis Overview:

The advent of digital platforms has revolutionized the way businesses interact with their customers. With the proliferation of online reviews, companies now have access to a wealth of data that contains valuable insights about customer sentiments towards their products. Sentiment analysis, a branch of natural language processing, offers a systematic approach to analyzing and quantifying these sentiments to support decision-making processes.

This thesis focuses on exploring the potential of sentiment analysis in analyzing customer reviews for product category expansion. By employing text mining and machine learning techniques, this study aims to develop a framework for extracting and analyzing sentiment from customer feedback to identify opportunities for expanding product categories. The research will address the following objectives: to investigate the current state of sentiment analysis in business applications, to explore the relationship between customer sentiments and product expansion, to develop a methodology for sentiment analysis using text mining and machine learning, to analyze the results of the sentiment analysis, and to provide recommendations for businesses based on the findings.

Through a comprehensive literature review, the study will synthesize existing knowledge on sentiment analysis, text mining techniques, machine learning algorithms, and their applications in business. The research methodology will outline the data collection, preprocessing, sentiment analysis techniques, machine learning models selection, and evaluation metrics used in the study. The discussion of findings will provide an in-depth analysis of customer reviews for product category expansion, including sentiment polarity detection, feature extraction, sentiment classification, model performance evaluation, and implications for businesses.

In conclusion, this thesis will contribute to the field of sentiment analysis by demonstrating the effectiveness of text mining and machine learning in extracting valuable insights from customer reviews. By leveraging sentiment analysis, businesses can gain a deeper understanding of customer sentiments and preferences, enabling them to make informed decisions about expanding their product categories. This research will serve as a valuable resource for companies seeking to utilize customer feedback for strategic growth and innovation.

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