nhull/random-forest-model

Random Forest Sentiment Analysis Model

This model is a Random Forest classifier trained on the TripAdvisor sentiment analysis dataset. It predicts the sentiment of hotel reviews on a 1-5 star scale. The model takes text input (hotel reviews) and outputs a sentiment rating from 1 to 5 stars.

Model Details

• —Model Type: Random Forest
• —Task: Sentiment Analysis
• —Input: A hotel review (text)
• —Output: Sentiment rating (1-5 stars)
• —Dataset Used: TripAdvisor sentiment dataset (balanced labels)

Intended Use

This model is designed to classify hotel reviews based on their sentiment. It assigns a star rating between 1 and 5 to a review, indicating the sentiment expressed in the review.

How to Use the Model

1. 1.Install the required dependencies:

    pip install joblib

1. 1.Download and load the model: You can download the model from Hugging Face and use it to predict sentiment.

Example code to download and use the model:

    from huggingface_hub import hf_hub_download
    import joblib

    # Download model from Hugging Face
    model_path = hf_hub_download(repo_id="your-username/random-forest-model", filename="random_forest_model.joblib")

    # Load the model
    model = joblib.load(model_path)

    # Predict sentiment of a review
    def predict_sentiment(review):
        return model.predict([review])[0]

    review = "This hotel was fantastic. The service was great and the room was clean."
    print(f"Predicted sentiment: {predict_sentiment(review)}")

1. 1.The model will return a sentiment rating between 1 and 5 stars, where:
2. 2.1: Very bad
3. 3.2: Bad
4. 4.3: Neutral
5. 5.4: Good
6. 6.5: Very good

Model Evaluation

• —Test Accuracy: 55.28% on the test set.

• —Classification Report (Test Set):

Cross-validation Scores:

Limitations

• —The model performs well on extreme ratings (1 and 5 stars) but struggles with intermediate ratings (2, 3, and 4 stars).
• —The model was trained on the TripAdvisor dataset and may not generalize well to reviews from other sources or domains.
• —The model does not handle aspects like sarcasm or humor well, and shorter reviews may lead to less accurate predictions.