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https://github.com/mohammadmoataz2/instasentiment

InstaSentiment is a powerful NLP (Natural Language Processing) project aimed at analyzing the sentiment of Instagram posts. 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.
https://github.com/mohammadmoataz2/instasentiment

api database fastapi insta instagram machine-learning nlp nltk postgresql power-bi python scraping sklearn

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InstaSentiment is a powerful NLP (Natural Language Processing) project aimed at analyzing the sentiment of Instagram posts. 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.

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README 

        
## InstaSentiment



InstaSentiment is a powerful NLP (Natural Language Processing) project aimed at analyzing the sentiment of Instagram posts. 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.

![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)



### Overview



InstaSentiment 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.



### Features



- **Web Application**: Users can input the URL of an Instagram post through a web interface.



![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/1c079e63-b17a-4327-bea9-3dabf4794396)



- **FastAPI Server Integration**: The web app communicates with a FastAPI server for efficient data processing.



- **Web Scraping**: Utilizes Scrapy for extracting comments from Instagram posts.



![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/87ddecc3-0839-4793-8921-8dc9af6831a1)

- **Sentiment Analysis**: Employs NLTK for NLP tasks, including tokenization and sentiment analysis.



- **Machine Learning**: Develops a sentiment prediction model using various machine learning algorithms.

- **Data Storage**: Stores comments and sentiment data in PostgreSQL for future analysis.



![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/0f331379-25b2-45b0-b5ed-7b8bd39e1188)

- **Power BI Dashboard**: Visualizes sentiment insights through a Power BI report for easy interpretation.

![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/c9d0bec5-5dac-42d3-93a3-7b6095f2fecb)

![image](https://github.com/MohammadMoataz2/Diabetes-Insight-and-Model-Mastery-with-KNIME-and-python/assets/123085286/e97edccb-1f94-419c-a24b-330be1ba5ded)

### Achievements



- Streamlined sentiment analysis of Instagram posts with an intuitive web interface.

- Leveraged NLP techniques and machine learning algorithms for accurate sentiment prediction.

- Provided users with comprehensive sentiment insights, including post-level positivity and negativity percentages.



### Technologies Used



- **Web Development**: HTML CSS JS

- **Python**

- **API**: FastAPI

- **Web Scraping**: BeautifulSoup (BS4), Selenium

- **Data Manipulation**: Pandas, NumPy

- **Data Visuz**: matplotlib,seaborn

- **Natural Language Processing (NLP)**: NLTK

- **Machine Learning**: Sentiment analysis algorithms,scikit-learn

- **Database**: PostgreSQL

- **Visualization**: Power BI



### Getting Started



To get started with InstaSentiment, follow these steps:



1. Clone the repository.

2. Install the required dependencies listed in `requirements.txt`.

3. Set up a PostgreSQL database and configure the connection.

4. Run the FastAPI server.

5. Access the web application and start analyzing Instagram post sentiments.



### Contributors



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Feel free to contribute, report issues, or suggest improvements! Let's make sentiment analysis on Instagram posts more accessible and insightful together.