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.

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.

• 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.

• 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.

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

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

---

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