Welcome to the Python TextBlob Twitter Sentiment Analysis repository! This project utilizes the TextBlob library in Python to analyze sentiments of tweets streamed from Twitter. It's a fascinating exploration of natural language processing, focusing on determining whether tweets carry a positive or negative sentiment.
This application taps into the Twitter API to fetch real-time tweets and uses TextBlob, a powerful Python library for processing textual data, to analyze and classify the sentiments expressed in these tweets. It's a useful resource for anyone interested in understanding how sentiment analysis works in the realm of social media.
- Real-time Twitter data streaming.
- Sentiment analysis using TextBlob.
- Classification of tweets into positive or negative sentiments.
Prerequisites Python 3.x Twitter API credentials Installation and Setup Clone the Repository
git clone git@github.com:uannabi/Python-dash-tw-sentiment.git
Install Required Libraries
Inside the project directory, install the required libraries using:
pip install -r requirements.txt
Twitter API Credentials
Update the credentials.py file with your Twitter API keys and tokens.
Run the Application
python run twsteam.py
Once the application is running, it will start fetching tweets in real time based on specified filters or keywords. Each tweet will be analyzed, and its sentiment (positive or negative) will be outputted.
Your contributions to enhance this sentiment analysis tool are highly appreciated. Feel free to fork this repository and submit your pull requests.