Stock News Sentiment Analysis

A lightweight Flask application that integrates news data, natural language processing, and sentiment analysis to assist in stock market interpretation. This project applies data science techniques to analyze stock related news sentiment and generate trader friendly insights.

Project Objective

To process stock market news using natural language understanding (NLP) and assess their potential market impact by:

• Extracting real time news from NewsAPI
• Performing sentiment polarity scoring using TextBlob
• Mapping scores to interpretable trader actions (e.g., Buy, Sell, Hold)
• Visualizing sentiment through color coded indicators

Dashboard Interface

Displays the overall sentiment trends for all stocks analyzed.

Sentiment and trader action recommendations for Microsoft (MSFT) under various sentiment conditions.

Positive sentiment analysis and recommended trading action for Tesla (TSLA).

Project Workflow

1. Data Acquisition

• Uses NewsAPI to fetch articles for major stock tickers like AAPL, TSLA, GOOGL, MSFT, etc.
• Filters articles using user-selected stock symbols via AJAX.

2. Text Preprocessing

• Extracts news article descriptions.
• Applies basic NLP filtering (e.g. removing empty descriptions).

3. Sentiment Analysis

• Uses TextBlob, a rule based NLP tool, to assign sentiment polarity scores ranging from -1.0 to +1.0.

4. Sentiment Interpretation

• Polarity score is mapped to:
  • A sentiment label (e.g., Slightly Positive)
  • A trading relevance suggestion (e.g., Buy, Sell)
  • A visual tag (background color for easier UX)

5. Data Filtering & Presentation

• Users can filter the news articles by:
  • Sentiment class (Neutral, Positive, Negative, etc.)
  • Trader relevance (Buy, Hold, Sell)
• Frontend dynamically reflects these selections using Flask routes + JSON + AJAX.

Libraries Used

Technique	Library	Purpose
NLP Preprocessing	TextBlob	Extract polarity sentiment from text
Sentiment Categorization	Rule-based	Convert numeric score into label
Data Mapping	Custom Logic	Assign trader actions via thresholds
Real-Time Data Streaming	NewsAPI	Fetch external data for DS processing

Tech Stack

• Backend: Python, Flask
• NLP / DS: TextBlob
• API: NewsAPI
• Frontend: HTML, CSS, JS (AJAX)
• Deployment Ready: Environment variables with python-dotenv

Future Enhancements

• Advanced Sentiment Analysis:

  • Incorporate more sophisticated NLP techniques, like spaCy or transformer models (e.g., BERT) for better sentiment accuracy.

• Trading Strategy Backtesting:

  • Develop a mechanism to backtest trading strategies based on sentiment analysis outcomes.
  • Simulate trading decisions (Buy, Hold, Sell) based on historical data and sentiment trends.

• Clustering & News Trend Analysis:

  • Use clustering techniques (e.g., K-means) to group similar news articles and detect trends in market sentiment over time.
  • Build timeline-based analysis for visualizing sentiment changes over multiple days/weeks.

• Additional Data Sources:

  • Integrate multiple news sources for a more comprehensive sentiment analysis and broader coverage of stock market news.