📈 Stock Vibe – Sentiment Analysis of Stock Forums

Stock Vibe is a data-driven project focused on sentiment analysis of stock market forums for 10 major companies.
The project analyzes investor sentiment from forum discussions and correlates it with historical stock price trends to uncover meaningful insights.

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🚀 Project Overview

Investors frequently express opinions on online stock forums.
This project aims to:

• Analyze sentiments (positive, negative, neutral) from forum data
• Compare sentiment trends with stock price movements
• Visualize insights through interactive and comparative graphs

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🏢 Companies Analyzed

The project covers sentiment analysis for 10 well-known companies, including:

• Adani Enterprises
• HDFC
• Hindustan Unilever
• ICICI Bank
• Infosys
• ITC
• Kotak Mahindra Bank
• Larsen & Toubro
• Reliance Industries
• Tata Consultancy Services (TCS)

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📊 Visualizations & Analysis

The following visualizations and trends are implemented:

• 📉 Stock Prices Over Years
• 😊 Sentiment Trends Over Years
• 🔁 Price vs Sentiment Correlation
• 🆚 Company-wise Comparison
• 📊 Positive / Negative / Neutral sentiment distribution

These visualizations help understand how public sentiment aligns (or diverges) from actual stock performance.

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🧠 Technologies Used

Backend

• Python
• Pandas
• NumPy
• Matplotlib / Seaborn
• NLP techniques for sentiment analysis

Frontend

• HTML
• CSS
• JavaScript
• Interactive charts and visual dashboards

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📂 Project Structure

StockVibe-project/ ├── Backend/ │ ├── app/ │ │ ├── data/ │ │ ├── sentiment.py │ │ ├── preprocess_once.py │ │ └── data_loader.py │ ├── requirements.txt │ └── run.py │ ├── Frontend/ │ ├── index.html │ ├── dashboard.html │ ├── compare.html │ └── visualizations.html │ └── README.md

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⚙️ How to Run the Project

Backend

cd Backend
pip install -r requirements.txt
python run.py

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Frontend

• Open index.html in a browser

• Navigate through dashboards and visualizations

📌 Key Insights

• Market sentiment often leads or lags price movement

• Strong sentiment spikes are visible around major market events

• Some companies show high sentiment volatility, while others remain stable

🎯 Future Improvements

• Live data scraping from forums

• Real-time sentiment tracking

• Machine learning-based sentiment classification

• Deployment as a full-stack web application

👨‍💻 Author

Alok Sophomore at Sitare University

⭐ If you find this project interesting, consider giving it a star!

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