Twitter Sentiment Analysis with Streamlit 😃

Welcome to the Twitter Sentiment Analysis project! This application allows you to analyze the sentiment of Twitter comments, predicting whether a comment is Positive 😊 or Negative 😞.

Features ✨

• Dual Model Predictions: This app uses two powerful machine learning models:

  • Logistic Regression
  • Naive Bayes

• Text Preprocessing: The app preprocesses the input text using stemming and stopword removal to improve prediction accuracy.

• User-Friendly Interface: Enter your comment, hit the "Predict Sentiment" button, and instantly see whether the sentiment is positive or negative!

• Visual Feedback: Sentiment predictions are displayed with clear, color-coded messages and expressive emojis.

How It Works 🔍

1. Text Preprocessing 🛠️

The input text goes through several preprocessing steps:

• Stemming: Words are reduced to their root forms using the Porter Stemmer.
• Stopword Removal: Commonly used English words that do not contribute much to the meaning (e.g., "the", "is") are removed.

2. Model Prediction 🤖

The preprocessed text is then transformed using a vectorizer (vectorizer.pkl) and fed into the machine learning models:

• Logistic Regression Model (logistic_regression_model.pkl)
• Naive Bayes Model (naive_bayes_model.pkl)

3. Output 🎯

The sentiment prediction is displayed on the screen:

• Positive Sentiment: Shown with a green background and a happy emoji 😊.
• Negative Sentiment: Shown with a red background and a sad emoji 😞.

How to Use the App 📝

1. Enter Your Comment: Type in the comment you want to analyze.
2. Predict Sentiment: Click the "Predict Sentiment" button.
3. View Results: See whether your comment is predicted to be positive or negative.

Installation and Setup ⚙️

1. Clone the Repository:

   git clone https://github.com/machinelearningprodigy/sentiment-analysis.git

2. Navigate to the Project Directory:

   cd twitter-sentiment-analysis

3. Install the Required Packages:

   pip install -r requirements.txt

4. Run the Streamlit App:

   streamlit run app.py

Live Demo 🚀

Check out the live demo of this app here: Twitter Sentiment Analysis

Acknowledgments 🙌

• NLTK: For providing tools for natural language processing.
• Scikit-learn: For machine learning model implementation.
• Streamlit: For making the web app creation process simple and intuitive.

Happy analyzing! 🎉