This project focuses on classifying restaurant reviews as positive or negative using a Random Forest classifier. The project includes text preprocessing, creating a Bag of Words model, training the classifier, and optimizing the model using Grid Search.
The project uses a dataset of restaurant reviews, processes the text to remove noise, creates a Bag of Words model, and trains a Random Forest classifier to predict the sentiment of reviews. The classifier is then optimized using Grid Search to find the best hyperparameters.
- Importing the libraries
- Importing the dataset
- Cleaning the texts
- Creating the Bag of Words model
- Splitting the dataset into the Training set and Test set
- Training the Random Forest model on the Training set
- Applying Grid Search to find the best model and the best parameters
- Predicting the Test set results
- Making the Confusion Matrix
- Predicting a single Review
The model achieved an accuracy of 75.2% on the test set. The best accuracy achieved using Grid Search is 79.33%.
- numpy
- matplotlib
- pandas
- nltk
- scikit-learn
You can install these packages using pip:
pip install numpy matplotlib pandas nltk scikit-learnThe dataset used is Restaurant_Reviews.tsv, which contains restaurant reviews and their corresponding sentiment. It has two columns:
Review: The text of the restaurant review. Liked: A binary value indicating whether the review is positive (1) or negative (0).
Clone the repository. Install the required libraries. Place the dataset Restaurant_Reviews.tsv in the project directory. Run the script to train the model and predict the sentiment of a review.
python ReviewSentimentAnalyzer.py