🧠 Natural Language Processing

A collection of Natural Language Processing (NLP) projects developed as part of academic coursework.
These projects focus on text preprocessing, feature extraction, sentiment analysis, and transformer-based models using Python and popular NLP libraries.

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📂 Projects Included

1️⃣ Financial Sentiment Analysis — BERT vs FinBERT

📌 Objective:
Compare the performance of BERT and FinBERT for sentiment classification in the financial domain.

📌 Key Highlights:

• Financial news and finance Q&A sentiment datasets
• Data normalization and preprocessing
• Transformer-based sentiment classification
• Comparative evaluation of domain-specific vs general language models

📌 Models Used:

• BERT
• FinBERT

📌 Contents:

• financial_sentiment_bert_vs_finbert.ipynb
• Raw and normalized financial sentiment datasets
• Detailed experimental report

📌 Datasets:

• Financial News Sentiment Dataset
• Financial Finance-QA Sentiment Dataset

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2️⃣ Twitter Sentiment Analysis

📌 Objective:
Analyze public sentiment from Twitter data using classical machine learning techniques.

📌 Key Highlights:

• Text cleaning and preprocessing
• Tokenization and feature extraction
• Sentiment classification (Positive / Negative)
• Model evaluation and performance analysis

📌 Contents:

• twitter_sentiment_analysis.ipynb
• Preprocessed and raw Twitter sentiment datasets
• Project report explaining methodology and results

📌 Dataset:

• Balanced Twitter sentiment dataset derived from publicly available sources

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🛠️ Technologies Used

• Python
• Natural Language Processing (NLP)
• Scikit-learn
• Transformers (Hugging Face)
• BERT / FinBERT
• Pandas, NumPy
• Jupyter Notebook

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📁 Repository Structure

natural-language-processing-projects/
│
├── financial-sentiment-analysis-bert-vs-finbert/
│   ├── financial_sentiment_bert_vs_finbert.ipynb
│   ├── README.md
│   └── report/
│       └── financial_sentiment_bert_vs_finbert.pdf
│
├── twitter-sentiment-analysis/
│   ├── twitter_sentiment_analysis.ipynb
│   ├── README.md
│   └── report/
│       └── twitter-sentiment-analysis.pdf
│
├── .gitignore
├── LICENSE
└── README.md

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🎓 Academic Context

These projects were completed as part of Natural Language Processing coursework, with emphasis on:

• Practical NLP pipelines
• Machine learning and transformer-based models
• Dataset handling and evaluation
• Research-style experimentation and reporting

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👤 Author

Sajidur Rahman Sajid
Computer Science & Engineering (CSE)
Aspiring AI / Machine Learning / NLP Engineer