Twitter Sentiment Analysis 🐦😃☹️

This project leverages Natural Language Processing (NLP) and Logistic Regression to classify the sentiment of tweets as either positive or negative. The model achieved an accuracy of 82%. Below you'll find detailed instructions on how to set up and run this project locally, as well as how to use the deployed Streamlit app.

Project Structure

Setup Instructions

1. Clone the Repository

git clone https://github.com/abhiiiman/Twitter_Sentiment_Analysis.git

cd Twitter_Sentiment_Analysis

2. Create a Virtual Environment

python -m venv venv

• Mac Users

source venv/bin/activate 

• Windows Users

venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Download NLTK Data

• In a Python shell, run:

import nltk
nltk.download('stopwords')
nltk.download('punkt')

5. Download the Dataset from here Download the Dataset

6. Run the Streamlit App

streamlit run app.py

Using the Deployed Streamlit App

1. Navigate to the Streamlit App Click Here
2. Enter Tweet Content
3. Predict Sentiment
4. Screenshots

• Negative Tweet

• Positive Tweet

Don't forget to give it a Star!

If you loved this project, give it a ⭐ on GitHub! It would make my codebase as happy as a positive tweet 😄.