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Introduction

As businesses and organizations continue to generate vast amounts of digital documents, the need for efficient document management systems has become increasingly important. Text classification, a subfield of natural language processing, has emerged as a powerful tool for organizing and classifying these documents automatically. By using machine learning algorithms, text classification can help streamline document management processes, improve search and retrieval capabilities, and enhance overall productivity.

This thesis explores the application of text classification for document management, with a focus on its benefits, challenges, and potential impact on businesses and organizations. The following chapters will delve into the background of the study, the problem statement, objectives, limitations, scope, significance, and structure of the thesis. Additionally, key terms related to text classification and document management will be defined to provide a clear understanding of the topic.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objectives 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 Text Classification
2.2 Document Management Systems
2.3 Machine Learning Algorithms for Text Classification
2.4 Applications of Text Classification in Document Management
2.5 Challenges in Text Classification for Document Management
2.6 Benefits of Text Classification in Document Management
2.7 Best Practices for Text Classification in Document Management
2.8 Case Studies and Examples
2.9 Future Trends in Text Classification for Document Management
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 Selection
3.5 Model Selection
3.6 Model Evaluation
3.7 Performance Metrics
3.8 Experimental Setup
3.9 Data Analysis Techniques

Chapter 4: Discussion of Findings
4.1 Analysis of Results
4.2 Interpretation of Findings
4.3 Comparison with Existing Literature
4.4 Implications for Document Management
4.5 Recommendations for Future Research
4.6 Limitations of the Study
4.7 Practical Applications of Text Classification in Document Management

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Implications for Practice
5.3 Contribution to Knowledge
5.4 Future Research Directions
5.5 Concluding Remarks

Overall, this thesis aims to provide a comprehensive overview of text classification for document management, highlighting its potential benefits and challenges. By examining relevant literature, discussing research methodology, presenting findings, and offering recommendations, this study seeks to contribute to the growing body of knowledge in this field.

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