https://github.com/jkanishkha0305/finsentinal

📈 Financial Sentiment Analysis 📊 Utilizing Scraping, LLM, and NLP for In-Depth Financial Insights! 💹💼📉 Unlock Market Trends 📈, Predict Price Movements 📉, and Dive Deep into Financial Data with Advanced Techniques! 🚀🌐💰 Discover the Power of Sentiment Analysis in Finance! 📈💡💪
https://github.com/jkanishkha0305/finsentinal

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📈 Financial Sentiment Analysis 📊 Utilizing Scraping, LLM, and NLP for In-Depth Financial Insights! 💹💼📉 Unlock Market Trends 📈, Predict Price Movements 📉, and Dive Deep into Financial Data with Advanced Techniques! 🚀🌐💰 Discover the Power of Sentiment Analysis in Finance! 📈💡💪

• Host: GitHub
• URL: https://github.com/jkanishkha0305/finsentinal
• Owner: Jkanishkha0305
• License: mit
• Created: 2023-07-06T21:48:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-15T12:20:15.000Z (about 1 year ago)
• Last Synced: 2023-10-16T16:50:54.768Z (about 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 8.48 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Financial Analysis through Sentiment Recognition 📊

Gain Insights into Financial Markets with Sentiment Analysis! 🚀

## Overview

This project is your gateway to a deeper understanding of financial trends and sentiments using state-of-the-art techniques. Dive into the financial world, perform sentiment analysis, and gain valuable insights. 💹

## Key Features

- 🌐 **Web Scraping:** Utilized Beautiful Soup to scrape financial articles from Financial Express, tapping into a wealth of financial data.

- 😃 **Transformers:** Leveraged the power of Hugging Face's transformers, including:

  - `bart-large-cnn` for Text Summarization 📰

  - `finbert-tone` for Sentiment Analysis

  - `bert-base-NER` for Named Entity Recognition 📚

- 📈 **Stock Analysis:** Explored the Yahoo Ticker list dataset to analyze stocks and uncover correlations between scraped articles and related companies.

- 🧠 **Forecasting Model:** Developed a forecasting model by correlating sentiments with related companies, unlocking predictive capabilities.

- 📊 **Data Visualization:** Created visually engaging graphs using Plotly to represent your findings.

## Installation

- Install the required packages using `pip install -r requirements.txt`. 🛠️

## Usage

1. Run the web scraping script to gather financial data.

2. Utilize transformers for sentiment analysis and named entity recognition.

3. Analyze stock data using the Yahoo Ticker list dataset.

4. Build your forecasting model.

5. Visualize your insights with Plotly.

## Contributors

- J.KANISHKHA 🙌

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. 📜

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Have questions or suggestions? Feel free to reach out! 📬