This is my NLP project. -RESTAURANT REVIEW

🎯 This project aims to analyze customer reviews from various restaurants to determine overall sentiment using Natural Language Processing (NLP) techniques.

🔍 The main objective of this project is to develop a model that accurately classifies the sentiment of restaurant reviews into positive or negative categories.

📋 Objectives:

Data Preprocessing: Clean and prepare the text data by removing special characters, stemming, and eliminating stop words.

Feature Extraction: Use the TF-IDF (Term Frequency-Inverse Document Frequency) vectorizer to convert the text data into numerical features that can be used by machine learning models.

Model Training: Train multiple machine learning models such as classifiers to predict the sentiment of the reviews.

Model Evaluation: Evaluate the performance of each model using metrics like accuracy, precision, recall, and F1-score. Additionally, confusion matrices are used to visualize the classification results.

💻 Tools and Libraries:

Python Libraries: pandas, nltk, scikit-learn, matplotlib, seaborn

Results:

· The performance of each model is compared, with classifiers showing the highest accuracy at approximately 50%. A word cloud is generated to visualize the most common words in the positive and negative reviews.!