Sentiment Analysis on Restaurant Reviews

This project performs Natural Language Processing (NLP) to classify restaurant reviews as positive or negative using a Naive Bayes classifier.

🧠 Model Overview

• Goal: Predict sentiment (positive/negative) of restaurant reviews.
• Dataset: 1,000 reviews from Restaurant_Reviews.tsv.

✨ Features

• Text cleaning and preprocessing pipeline.
• Bag of Words model with feature limitation.
• Sentiment prediction using Gaussian Naive Bayes.
• Performance evaluation using accuracy and confusion matrix.

🧹 Data Preprocessing

• Removed non-alphabetic characters.
• Converted to lowercase.
• Tokenized into words.
• Removed stopwords (excluding "not").
• Applied Porter Stemming.
• Reconstructed into cleaned text corpus.

🧰 Feature Extraction

• Used Bag of Words model with CountVectorizer.
• Limited features to top 1500 words.

🤖 Modeling

• Classifier: Gaussian Naive Bayes
• Train/Test Split: 80/20

📊 Evaluation

• Confusion Matrix and Accuracy Score used to assess performance.

🛠 Installation

1. Clone this repository:

git clone https://github.com/MaddyRizvi/Natural-Language-Processing_sentiment_analysis.git
cd Natural-Language-Processing_sentiment_analysis

2. Install dependencies:

pip install -r requirements.txt

🚀 Usage

1. Ensure you have the dataset Restaurant_Reviews.tsv in the project directory.
2. Run the script:

python natural_language_processing.py

📁 Files

• Restaurant_Reviews.tsv: Dataset file.
• natural_language_processing.py: Main script for training and testing.
• README.md: Project overview and instructions (this file).
• CONTRIBUTING.md: Guidelines for contributing.

✅ Requirements

• numpy
• pandas
• matplotlib
• scikit-learn
• nltk

🙌 Contributing

We welcome contributions! Please read the CONTRIBUTING.md file for guidelines.

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