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🐦 Sentiment Analysis of Tweets

This project performs Sentiment Analysis on Tweets using Machine Learning and Natural Language Processing (NLP) techniques.
It classifies tweets into:

  • ✅ Positive
  • ❌ Negative
  • 😐 Neutral

The system includes data preprocessing, text cleaning, feature extraction, model training, and performance evaluation.

📌 Project Description

Social media platforms like Twitter generate large volumes of opinion-based textual data.
This project aims to automatically determine the sentiment expressed in tweets by analyzing their textual content.

Sentiment analysis is useful for:

  • Customer feedback analysis
  • Product review monitoring
  • Brand reputation management
  • Public opinion mining
  • Social media analytics

🛠️ Technologies Used

  • Python
  • Pandas
  • NumPy
  • Scikit-learn
  • Matplotlib
  • Seaborn
  • re (Regular Expressions)
  • tqdm
  • pysentimiento
  • Natural Language Processing (NLP)

📂 Project Structure

Sentiment_Analysis_of_Tweet/
│
├── Sentiment_Analysis_of_Tweet.ipynb   # Main implementation file
├── Tweets.csv                          # Dataset used
└── README.md                           # Project documentation

🧹 Data Preprocessing Steps

The following preprocessing techniques are applied to clean tweet text:

  • Convert text to lowercase
  • Remove URLs
  • Remove mentions (@username)
  • Remove hashtags
  • Remove punctuation
  • Remove stopwords
  • Tokenization
  • Lemmatization
  • Remove special characters using Regular Expressions

🧠 Model Training

The cleaned data is transformed into numerical features using:

  • TF-IDF Vectorization

Machine Learning algorithms are then applied for sentiment classification.

📊 Performance Evaluation

Model performance is evaluated using:

  • Accuracy Score
  • Confusion Matrix
  • Classification Report

Visualization is done using Matplotlib and Seaborn.

▶️ How to Run the Project

Step 1: Clone the Repository

git clone https://github.com/your-username/Sentiment_Analysis_of_Tweet.git
cd Sentiment_Analysis_of_Tweet

Step 2: Install Required Libraries

pip install pandas numpy scikit-learn matplotlib seaborn tqdm pysentimiento nltk

Step 3: Open Jupyter Notebook

jupyter notebook

Open:

Sentiment_Analysis_of_Tweet.ipynb

Run all cells to preprocess data, train the model, and evaluate performance.

📈 Output

The trained model predicts whether a tweet expresses:

  • Positive Sentiment
  • Negative Sentiment
  • Neutral Sentiment

🔮 Future Enhancements

  • Deep Learning Models (LSTM, BERT)
  • Real-time Twitter API integration
  • Web-based GUI using Streamlit
  • Deployment on cloud platforms

📄 License

This project is developed for educational purposes.

Developed by:L Jitendra Kumar