BSc IT Project Guide: Financial News Sentiment Analysis
Project Title: Financial News Sentiment Analysis
1. Project Overview
This project focuses on developing a sentiment analysis system specifically tailored to financial news articles. The goal is to predict market reactions based on the sentiment of the news. Natural Language Processing (NLP) techniques and machine learning models will be employed to classify news sentiment as positive, negative, or neutral, providing insights into potential market movements.
2. Objectives
- Collect and preprocess financial news data.
- Perform sentiment analysis using machine learning or deep learning models.
- Train the model to classify sentiment into positive, negative, or neutral.
- Visualize sentiment trends to predict market movements.
- Evaluate model accuracy using standard metrics.
3. Tools and Technologies
- Programming Language: Python
- Libraries: NLTK, spaCy, Scikit-learn, TensorFlow/Keras, pandas, matplotlib
- Data Sources: Financial news APIs or datasets (e.g., Yahoo Finance, Reuters)
- IDE: Jupyter Notebook / VS Code
4. System Architecture
The system architecture includes modules for data ingestion, preprocessing,
sentiment classification, and result visualization. News articles are fetched
and preprocessed to remove noise, then passed to a classifier that predicts
sentiment. Results are visualized to identify patterns and assist in decision
making.
5. Methodology
1. Data Collection: Use web scraping or APIs to gather financial news.
2. Data Preprocessing: Clean text, tokenize, remove stop words,
stemming/lemmatization.
3. Feature Extraction: Use TF-IDF or word embeddings.
4. Model Building: Train ML/DL models (e.g., Logistic Regression, LSTM).
5. Evaluation: Use metrics like accuracy, precision, recall, F1-score.
6. Deployment: Optional web interface for input and output visualization.
6. Expected Outcomes
- A trained sentiment analysis model for financial news.
- A dashboard or interface displaying sentiment trends.
- Improved understanding of market sentiment based on news inputs.
7. Future Enhancements
- Integrate real-time news feeds for live sentiment tracking.
- Incorporate stock market data for correlation analysis.
- Use ensemble models for higher accuracy.