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Thesis Overview

Title: Sentiment Analysis of Customer Support Interactions using Text Mining and Deep Learning

Introduction:

In today’s digital age, customer support interactions play a crucial role in determining the success of businesses. Understanding customer sentiments and feedback is essential for companies to improve their services and products. Sentiment analysis, a subfield of natural language processing, allows businesses to analyze customer interactions to gain insights into customer sentiments. This thesis focuses on applying text mining and deep learning techniques to analyze customer support interactions and extract valuable insights from the data.

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 in Natural Language Processing
2.4 Customer Support Interactions
2.5 Sentiment Analysis in Customer Support
2.6 Applications of Sentiment Analysis
2.7 Challenges in Sentiment Analysis
2.8 Previous Studies on Sentiment Analysis
2.9 Future Trends in Sentiment Analysis
2.10 Gaps in Existing Literature

Chapter 3: Research Methodology

3.1 Data Collection
3.2 Data Preprocessing
3.3 Feature Extraction
3.4 Model Selection
3.5 Training and Testing
3.6 Evaluation Metrics
3.7 Ethical Considerations
3.8 Software and Tools Used

Chapter 4: Discussion of Findings

4.1 Analysis of Customer Support Interactions
4.2 Sentiment Analysis Results
4.3 Comparison of Text Mining and Deep Learning Techniques
4.4 Implications of Findings
4.5 Recommendations for Businesses
4.6 Future Research Directions

Chapter 5: Conclusion and Summary

5.1 Summary of Findings
5.2 Conclusions
5.3 Contributions of the Study
5.4 Limitations and Future Research Opportunities

This thesis aims to provide a comprehensive analysis of sentiment analysis of customer support interactions using text mining and deep learning techniques. By examining customer feedback and sentiments, businesses can enhance their customer support services and improve overall customer satisfaction.

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