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

Last synced: 16 days ago
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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.

• Host: GitHub
• URL: https://github.com/mohammadmoataz2/instasentiment
• Owner: MohammadMoataz2
• Created: 2024-05-16T15:46:06.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-20T14:33:56.000Z (about 1 year ago)
• Last Synced: 2025-03-31T08:12:21.112Z (about 2 months ago)
• Topics: api, database, fastapi, insta, instagram, machine-learning, nlp, nltk, postgresql, power-bi, python, scraping, sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 9.74 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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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.