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It provides users with valuable insights into the positivity and negativity of comments on a given post URL and store valuable information in PostgreSQL server then Visualize with power bi.\n![Blue and Yellow Modern Data Analysis Presentation](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/4167606d-cd93-425d-af7e-fc375db7d04d)\n\n\n### Overview\n\nInstaSentiment is designed to seamlessly analyze sentiment through a user-friendly web interface. It employs a combination of web scraping, NLP techniques, machine learning algorithms, and data visualization to deliver comprehensive sentiment analysis results.\n\n### Features\n\n- **Web Application**: Users can input the URL of an Instagram post through a web interface.\n\n![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/1c079e63-b17a-4327-bea9-3dabf4794396)\n\n- **FastAPI Server Integration**: The web app communicates with a FastAPI server for efficient data processing.\n\n\n- **Web Scraping**: Utilizes Scrapy for extracting comments from Instagram posts.\n\n\n\n![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/87ddecc3-0839-4793-8921-8dc9af6831a1)\n- **Sentiment Analysis**: Employs NLTK for NLP tasks, including tokenization and sentiment analysis.\n\n\n- **Machine Learning**: Develops a sentiment prediction model using various machine learning algorithms.\n- **Data Storage**: Stores comments and sentiment data in PostgreSQL for future analysis.\n\n![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/0f331379-25b2-45b0-b5ed-7b8bd39e1188)\n- **Power BI Dashboard**: Visualizes sentiment insights through a Power BI report for easy interpretation.\n![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/c9d0bec5-5dac-42d3-93a3-7b6095f2fecb)\n![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/e97edccb-1f94-419c-a24b-330be1ba5ded)\n### Achievements\n\n- Streamlined sentiment analysis of Instagram posts with an intuitive web interface.\n- Leveraged NLP techniques and machine learning algorithms for accurate sentiment prediction.\n- Provided users with comprehensive sentiment insights, including post-level positivity and negativity percentages.\n\n### Technologies Used\n\n- **Web Development**: HTML CSS JS\n- **Python**\n- **API**: FastAPI\n- **Web Scraping**: BeautifulSoup (BS4), Selenium\n- **Data Manipulation**: Pandas, NumPy\n- **Data Visuz**: matplotlib,seaborn\n- **Natural Language Processing (NLP)**: NLTK\n- **Machine Learning**: Sentiment analysis algorithms,scikit-learn\n- **Database**: PostgreSQL\n- **Visualization**: Power BI\n\n### Getting Started\n\nTo get started with InstaSentiment, follow these steps:\n\n1. Clone the repository.\n2. Install the required dependencies listed in `requirements.txt`.\n3. Set up a PostgreSQL database and configure the connection.\n4. Run the FastAPI server.\n5. Access the web application and start analyzing Instagram post sentiments.\n\n### Contributors\n\n\n\n\n---\n\nFeel free to contribute, report issues, or suggest improvements! Let's make sentiment analysis on Instagram posts more accessible and insightful together.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammadmoataz2%2Finstasentiment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohammadmoataz2%2Finstasentiment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammadmoataz2%2Finstasentiment/lists"}