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Introduction

The advent of social media and online review platforms has led to an explosion of customer feedback that can be used to improve service quality. However, analyzing this vast amount of unstructured text data manually is not feasible. Sentiment analysis using text mining and machine learning techniques provides a solution to this problem by automatically categorizing customer feedback into positive, negative, and neutral sentiments.

This thesis focuses on sentiment analysis of customer feedback for service recovery using text mining and machine learning. The study aims to develop a comprehensive framework for analyzing customer feedback data to identify areas for service improvement and enhance customer satisfaction. By applying advanced analytics, organizations can prioritize and address negative feedback promptly, leading to effective service recovery 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 Importance of Customer Feedback in Service Recovery
2.2 Sentiment Analysis Techniques
2.3 Text Mining Approaches
2.4 Machine Learning Algorithms for Sentiment Analysis
2.5 Customer Feedback Classification Models
2.6 Service Recovery Strategies
2.7 Integration of Sentiment Analysis in Service Recovery
2.8 Challenges in Sentiment Analysis of Customer Feedback
2.9 Best Practices in Sentiment Analysis for Service Recovery
2.10 Previous Studies on Sentiment Analysis of Customer Feedback

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection and Preprocessing
3.3 Sentiment Analysis Framework Development
3.4 Machine Learning Model Selection
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Data Analysis Techniques
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Customer Feedback Data
4.2 Sentiment Analysis Results
4.3 Service Recovery Recommendations
4.4 Comparison of Machine Learning Algorithms
4.5 Implications for Service Improvement
4.6 Managerial Insights
4.7 Future Research Directions

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

Thesis Overview

Sentiment analysis of customer feedback is crucial for organizations to understand customer opinions and feelings towards their products and services. In the context of service recovery, where customer satisfaction plays a critical role in retaining customers and building brand loyalty, analyzing customer feedback is essential. This thesis explores the application of text mining and machine learning techniques to automatically categorize customer feedback into positive, negative, and neutral sentiments for effective service recovery strategies.

The literature review will provide a comprehensive overview of the importance of customer feedback in service recovery, sentiment analysis techniques, machine learning algorithms, best practices in sentiment analysis, and previous studies in this domain. The research methodology section will outline the research design, data collection, preprocessing steps, sentiment analysis framework development, machine learning model selection, evaluation metrics, and ethical considerations.

The discussion of findings chapter will present the analysis of customer feedback data, sentiment analysis results, service recovery recommendations, comparison of machine learning algorithms, implications for service improvement, managerial insights, and future research directions. Finally, the conclusion chapter will summarize the findings, discuss the contributions of the study, provide managerial implications, suggest limitations and recommendations for future research, and conclude the thesis on sentiment analysis of customer feedback for service recovery.

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