🍽️ Sentiment Analysis of Restaurant Reviews

This project performs sentiment analysis on restaurant reviews using 🧠 Natural Language Processing (NLP) and a 🤖 Naive Bayes classifier. It classifies each review as positive ✅ or negative ❌ based on its content.

✨ Features

• 🧹 Clean and preprocess text data
• ❌ Remove stopwords using NLTK
• 🌱 Apply stemming using PorterStemmer
• 🧾 Extract features with CountVectorizer
• 🤖 Train a MultinomialNB sentiment classifier
• 📊 Evaluate model performance with a confusion matrix and accuracy score
• 📝 Predict sentiment for a custom review

🧾 Requirements

Symbol	Requirement (Library)
📄	pandas
🔍	re
📘	nltk
🧾	sklearn.feature_extraction.text – CountVectorizer
🤖	sklearn.naive_bayes
✂️	sklearn.model_selection
📊	sklearn.metrics

📁 Dataset

• File: Restaurant_Reviews.tsv

📌 Make sure the dataset is placed in the same folder as the notebook.

⚙️ Workflow

1. 📥 Load dataset using pandas
2. 🧹 Clean text: remove non-letter characters, convert to lowercase
3. ❌ Remove stopwords using NLTK
4. 🌱 Apply stemming with PorterStemmer
5. 🧾 Convert text to features using CountVectorizer
6. ✂️ Split data into training and testing sets
7. 🤖 Train a Naive Bayes classifier
8. 📊 Evaluate with confusion matrix & accuracy
9. 🔍 Predict sentiment for a new review

🚀 How to Run

1️⃣ Clone the Repository

git clone https://github.com/BhagyashreeMohalkar/Sentiment-Analysis-Restaurant.git
cd Sentiment-Analysis-Restaurant

2️⃣ Install Required Libraries

pip install nltk scikit-learn pandas

3️⃣ Launch the Notebook

jupyter notebook Sentiment_Analysis_of_Restaurant_Reviews.ipynb

📝 Ensure that Restaurant_Reviews.tsv is in the same directory.

📊 Sample Output

🧾 Confusion Matrix

[[91  9]
 [17 83]]

🎯 Accuracy Score

0.87

👩‍💻 Author

Bhagyashree Savata Mohalkar
🎓 Electronics & Computer Engineer

🔗 LinkedIn | 🐙 GitHub

🌟 Support

If you found this project helpful, don’t forget to ⭐ the repository. Your support encourages more projects like this! 😊

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