Real-Time Sentiment Analysis of Twitter Data

• Project Overview
• Features
• Technologies Used

Project Overview

This project involves creating a Python application that performs real-time sentiment analysis on Twitter data. It analyzes tweets on various AI-related topics, assesses their sentiment, and visualizes the results in an interactive dashboard using Streamlit. The data is fetched in real-time using Tweepy and stored in a Snowflake database.

Features

• 'Real-time' Twitter data fetching and analysis (updates every one minute).
• Sentiment analysis of tweets related to AI.
• Interactive dashboard built with Streamlit for data visualization.
• Data storage and retrieval using Snowflake.
• Automated tweet fetching using a scheduled cron job.

Technologies Used

• Python
• NLTK (for lemmatization)
• TextBlob (for sentiment model)
• SQL
• Snowflake
• AWS (EC2 instance)
• Streamlit (for the dashboard - Note that I am using the Free Tier of the Twitter API, which may affect the availability of real-time data.)
• Twitter API

Prerequisites

• Python 3.9
• Twitter Developer Account and API credentials
• Snowflake account
• Streamlit
• Additional Python libraries: tweepy, textblob, pandas, numpy, matplotlib

Installation

1. Clone the repository:

git clone https://github.com/melissamullen/twitter-sentiment-analysis.git

2. Navigate to the project directory:

cd twitter-sentiment-analysis

3. Install required Python packages:

pip install -r requirements.txt

Configuration

1. Set up your Twitter API credentials and Snowflake connection details in an .env file or use environment variables.

Running the Dashboard

To run the Streamlit dashboard locally, execute the following command:

streamlit run apps/sentiment_dashboard.py

Deployment

• The dashboard is deployed on Streamlit Sharing.
• The tweet fetching script is set up as a cron job on an AWS EC2 instance.