This repository contains a machine learning model for sentiment analysis using a Naive Bayes classifier. The model is trained on a dataset of restaurant reviews and can predict whether a given review is positive or negative.

The dataset used for training and testing is "Restaurant_Reviews.tsv". It includes reviews along with their corresponding labels (1 for positive, 0 for negative).

The reviews undergo a series of preprocessing steps, including:

• Removal of special characters
• Conversion to lowercase
• Tokenization
• Removal of stopwords
• Stemming

The preprocessed reviews are then transformed into a Bag of Words model using the CountVectorizer from scikit-learn.

The Naive Bayes classifier (MultinomialNB) is used for training the model. The dataset is split into training and testing sets, and the model is evaluated based on accuracy, precision, recall, and a confusion matrix.

The alpha hyperparameter of the Naive Bayes classifier is tuned for better performance.

A function predict_sentiment is provided to predict the sentiment of new reviews. Examples are included in the code for demonstration.

The best accuracy achieved during hyperparameter tuning is [insert accuracy]% with an alpha value of [insert alpha value].