Title: Financial News Summarizer
1. Problem Statement: The Financial News Summarizer aims to address the challenge of efficiently summarizing financial news articles related to specific stock tickers. The goal is to provide users with concise summaries of relevant news articles, along with sentiment analysis, to help them quickly understand the overall sentiment and key points about a particular stock.
2. Solution: The solution involves building a web application that allows users to enter a stock ticker. The application will then search for recent news articles related to the given stock ticker, extract relevant content, generate summaries using the LSA summarization technique, and analyze sentiment using the TextBlob library. The summarized information, sentiment analysis results, and relevant URLs will be displayed to the user.
3. Tech Stack Used:
- Python (Flask framework)
- Beautiful Soup (for web scraping)
- TextBlob (for sentiment analysis)
- Sumy (for text summarization using LSA)
- HTML/CSS (for web interface)
4. Screenshots:
5. Individual/Team: Indivisual
6. Duration: 1 month
8. Observations: During the development of the Financial News Summarizer, several key observations were made:
- Efficient web scraping and data cleaning are crucial for obtaining accurate and relevant news articles.
- The choice of summarization technique impacts the quality and conciseness of the generated summaries.
- Sentiment analysis provides valuable insights into the overall sentiment of the news articles.
9. Link to Project: The Financial News Summarizer project can be accessed at [Project URL].