🐦 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.

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📌 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

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🛠️ Technologies Used

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

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📂 Project Structure

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

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🧹 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

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🧠 Model Training

The cleaned data is transformed into numerical features using:

• TF-IDF Vectorization

Machine Learning algorithms are then applied for sentiment classification.

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📊 Performance Evaluation

Model performance is evaluated using:

• Accuracy Score
• Confusion Matrix
• Classification Report

Visualization is done using Matplotlib and Seaborn.

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▶️ 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.

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📈 Output

The trained model predicts whether a tweet expresses:

• Positive Sentiment
• Negative Sentiment
• Neutral Sentiment

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🔮 Future Enhancements

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

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📄 License

This project is developed for educational purposes.

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Developed by:L Jitendra Kumar