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Introduction

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on the interaction between human language and computers. Document classification is a key task in NLP, where documents are automatically assigned to predefined categories based on their content. This process is essential for organizing and managing large collections of textual data efficiently. In recent years, the advancement of machine learning techniques has significantly improved the accuracy and effectiveness of document classification systems.

This thesis aims to explore the application of NLP techniques for document classification. The research will focus on developing a model that can accurately classify documents into relevant categories, using a combination of text processing, feature engineering, and machine learning algorithms. The study will also investigate the impact of different factors such as document length, vocabulary size, and class distribution on the performance of the classification model.

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 Introduction to NLP
2.2 Document classification techniques
2.3 Text preprocessing methods
2.4 Feature selection and extraction
2.5 Machine learning algorithms for classification
2.6 Evaluation metrics for document classification
2.7 Recent advancements in document classification
2.8 Applications of document classification
2.9 Challenges in document classification
2.10 Summary of literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection and preprocessing
3.3 Feature engineering techniques
3.4 Model selection and training
3.5 Performance evaluation
3.6 Experimental setup
3.7 Statistical analysis
3.8 Ethical considerations

Chapter 4: Discussion of Findings
4.1 Analysis of experimental results
4.2 Impact of document length on classification
4.3 Effect of vocabulary size on classification accuracy
4.4 Influence of class distribution on model performance
4.5 Comparison of different machine learning algorithms
4.6 Interpretation of feature importance
4.7 Limitations of the study
4.8 Future research directions

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Implications of the study
5.3 Contributions to NLP research
5.4 Practical applications of the classification model
5.5 Recommendations for future work
5.6 Conclusion

Thesis Overview

The use of natural language processing (NLP) for document classification has gained significant attention in recent years due to the exponential growth of textual data. This thesis aims to investigate the effectiveness of NLP techniques in classifying documents into relevant categories. The study will focus on developing a model that can accurately classify documents based on their content, using a combination of text processing, feature engineering, and machine learning algorithms.

Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 presents a comprehensive literature review on NLP, document classification techniques, feature selection, machine learning algorithms, evaluation metrics, recent advancements, applications, and challenges in document classification.

Chapter 3 describes the research methodology, including research design, data collection, preprocessing, feature engineering, model selection, training, performance evaluation, experimental setup, statistical analysis, and ethical considerations. Chapter 4 discusses the findings of the study, analyzing experimental results, the impact of document length, vocabulary size, and class distribution on classification accuracy, comparison of machine learning algorithms, interpretation of feature importance, limitations, and future research directions.

Chapter 5 concludes the thesis by summarizing key findings, implications of the study, contributions to NLP research, practical applications of the classification model, recommendations for future work, and a final conclusion. The thesis aims to contribute to the advancement of NLP research and provide insights into the development of robust document classification systems.

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