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this is a web application using Flask and JavaScript to perform data scraping from a specific website every morning. The extracted data is then subjected to sentiment analysis using the TextBlob library. The results of this analysis are visually presented through interactive charts on the user interface.

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WebScrapping-Dash

this is a web application using Flask and JavaScript to perform data scraping from a specific website every morning. The extracted data is then subjected to sentiment analysis using the TextBlob library. The results of this analysis are visually presented through interactive charts on the user interface.

Key Features

1-Automatic Scraping: The project integrates an automatic scraping mechanism that retrieves relevant data from the target site every morning.

2-Sentiment Analysis: The extracted data undergoes sentiment analysis using TextBlob, determining the overall tone of the collected information.

3-Data Conversion: The results of sentiment analysis are converted into a format suitable for graphical display on the frontend.

4-Interactive Visualization: The user interface offers interactive charts that clearly represent the statistics resulting from sentiment analysis.

5-History: A history of previous analyses is maintained, allowing users to track the evolution of sentiments over time.

Technologies Used

Backend: Flask (Python) Frontend: JavaScript, HTML, CSS Scraping: Beautiful Soup (Python) Sentiment Analysis: TextBlob (Python) Charts: JavaScript charting libraries Chart.js

How to Contribute

We welcome contributions from the community to enhance existing features, resolve issues, or add new ideas. Please refer to our contribution guide for more information.

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this is a web application using Flask and JavaScript to perform data scraping from a specific website every morning. The extracted data is then subjected to sentiment analysis using the TextBlob library. The results of this analysis are visually presented through interactive charts on the user interface.

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