BSc IT Project Guide : Sentiment Analysis for Finance
1. Objective
To develop a system that analyzes the sentiment of financial news articles and social media data to help predict market trends. This project leverages natural language processing (NLP) techniques to classify news as positive, negative, or neutral and correlate them with stock movements.
2. Tools and Technologies Used
- Python
- Natural Language Toolkit (NLTK)
- TextBlob or VADER Sentiment Analyzer
- Scikit-learn
- Pandas, NumPy
- Matplotlib/Seaborn
- Jupyter Notebook or any Python IDE
3. Modules of the Project
- Data Collection Module (scraping financial news from
websites or using APIs)
- Data Cleaning and Preprocessing Module
- Sentiment Analysis Module
- Visualization Module
- Trend Prediction Correlation Module
4. System Design
The system architecture consists of the following
components:
- Input layer to gather data from various sources
- Preprocessing pipeline to clean and prepare the data
- Sentiment classification layer using NLP models
- Visualization and analytics dashboard to show sentiment trends
- (Optional) A machine learning layer to correlate sentiment with stock
movement trends
5. Implementation Steps
1. Collect and preprocess financial news datasets.
2. Perform tokenization, stop word removal, and lemmatization.
3. Use a sentiment analysis model (e.g., VADER/TextBlob) to classify sentiment.
4. Store and visualize results using Python plotting libraries.
5. Evaluate sentiment trends against stock price movements.
6. Expected Outcome
The system will display the sentiment classification of financial news and social media content. It will also show sentiment trends and potential correlation with market movements, aiding traders and analysts.
7. Conclusion
Sentiment Analysis for Finance provides an efficient way to extract market sentiment from unstructured text, assisting in investment decisions and trend forecasting. It integrates NLP and data visualization effectively for financial analysis.