🍽️ Restaurant Review Sentiment Analysis 📝

📖 Important Note

For a better understanding of the project, please check the Google Colab file 📄 uploaded in this repository. It contains detailed explanations and execution steps to help you grasp the workflow more effectively.

This repository contains a sentiment analysis project using Natural Language Processing (NLP) and a Naive Bayes classifier to classify restaurant reviews as positive 👍 or negative 👎.

🔍 Overview

• 📂 The dataset consists of restaurant reviews stored in a TSV file.
• 🧹 Text preprocessing is performed to clean and prepare the data.
• 📊 A Bag of Words (BoW) model is used to convert text data into numerical format.
• 🤖 A Naive Bayes classifier is trained on the dataset to perform sentiment classification.
• 📈 Model evaluation is done using a confusion matrix and accuracy score.

🛠️ Technologies Used

• 🐍 Python
• 🗂️ Pandas
• 🔢 NumPy
• 📉 Matplotlib
• 📝 NLTK (Natural Language Toolkit)
• 🤖 Scikit-learn

⚙️ Installation

Ensure you have Python installed and set up a virtual environment (optional but recommended).

1. 🚀 Clone this repository:

   git clone https://github.com/yourusername/restaurant-review-nlp.git
   cd restaurant-review-nlp

2. 📦 Install dependencies:

   pip install -r requirements.txt

3. 📥 Download the necessary NLTK stopwords:

   import nltk
   nltk.download('stopwords')

▶️ Usage

Run the script to preprocess the dataset, train the Naive Bayes model, and evaluate performance:

python sentiment_analysis.py

📊 Dataset

The dataset used is Restaurant_Reviews.tsv, which contains:

• 🗣️ A column Review with customer reviews.
• ✅ A column Liked (1 for positive, 0 for negative sentiment).

🏗️ Steps in the Code

1. 📥 Load Dataset: Read the Restaurant_Reviews.tsv file.
2. 🧼 Text Cleaning & Preprocessing:
   • Remove special characters, convert text to lowercase.
   • Remove stopwords (except negations like "not").
   • Apply stemming using PorterStemmer.
3. 📊 Feature Extraction:
   • Use CountVectorizer to create a Bag of Words model.
   • Convert text into a numerical matrix representation.
4. 📑 Train-Test Split:
   • 80% training, 20% testing.
5. 🤖 Train Model:
   • Train a Multinomial Naive Bayes classifier.
6. 📈 Evaluate Model:
   • Predict test data.
   • Compute accuracy score and confusion matrix.

📊 Model Performance

The script prints:

• 🟩 Confusion matrix for training and test datasets.
• 🎯 Accuracy score of the classifier.

🤝 Contribution

Feel free to fork this repository, submit issues, and contribute with improvements! 🚀

📜 License

This project is open-source and available under the MIT License.