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Introduction

Natural Language Processing (NLP) is a rapidly growing field in the domain of artificial intelligence that focuses on the interaction between computers and human language. One of the applications of NLP is automated text classification and categorization, which involves the categorization of text documents into predefined classes based on their content. This thesis aims to investigate the use of NLP for automated text classification and categorization to improve the efficiency and accuracy of this process.

Chapter One: 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 Two: Literature Review
2.1 Overview of Natural Language Processing
2.2 Text Classification and Categorization Techniques
2.3 NLP Tools and Libraries
2.4 Applications of NLP in Automated Text Classification
2.5 Challenges in NLP for Text Classification
2.6 NLP in Machine Learning Models
2.7 NLP in Deep Learning Models
2.8 Performance Metrics for Text Classification
2.9 Previous Studies on NLP for Text Classification
2.10 Gaps in the Literature

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Preprocessing
3.4 Feature Extraction
3.5 Model Selection
3.6 Model Training
3.7 Model Evaluation
3.8 Experimental Setup
3.9 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Analysis of Results
4.2 Comparison of Different NLP Techniques
4.3 Interpretation of Results
4.4 Implications of Findings
4.5 Practical Applications
4.6 Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Conclusion
5.2 Summary of Findings
5.3 Contributions of the Study
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview

The use of natural language processing for automated text classification and categorization has gained significant attention in recent years due to the growing volume of text data available online. This thesis aims to explore the effectiveness of NLP techniques in classifying and categorizing text documents accurately and efficiently.

Chapter one provides an introduction to the research topic, including background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter two presents a comprehensive review of the existing literature on NLP, text classification techniques, NLP tools, applications, challenges, machine learning, deep learning, performance metrics, previous studies, and gaps in the literature.

Chapter three discusses the research methodology, including research design, data collection, preprocessing, feature extraction, model selection, training, evaluation, experimental setup, and data analysis techniques. Chapter four presents a detailed discussion of the findings, analysis of results, comparison of NLP techniques, interpretation, implications, practical applications, and future research directions.

Chapter five concludes the thesis with a summary of findings, contributions of the study, recommendations for future research, and a conclusion. By investigating the use of NLP for automated text classification and categorization, this thesis aims to contribute to the advancement of the field and provide insights for researchers and practitioners in the domain of NLP and text analysis.

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