README.md

metadata

license: mit
datasets:
  - nhull/tripadvisor-split-dataset-v2
language:
  - en
pipeline_tag: text-classification
tags:
  - sentiment-analysis
  - logistic-regression
  - text-classification
  - hotel-reviews
  - tripadvisor
  - nlp

Logistic Regression Sentiment Analysis Model

This model is a Logistic Regression 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: Logistic Regression
• 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. Install the required dependencies:

   pip install joblib
   

2. 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/logistic-regression-model", filename="logistic_regression_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)}")
   

3. The model will return a sentiment rating between 1 and 5 stars, where:

   • 1: Very bad
   • 2: Bad
   • 3: Neutral
   • 4: Good
   • 5: Very good

Model Evaluation

• Test Accuracy: 61.05% on the test set.

• Classification Report (Test Set):

Label	Precision	Recall	F1-score	Support
1.0	0.70	0.73	0.71	1600
2.0	0.52	0.50	0.51	1600
3.0	0.57	0.54	0.55	1600
4.0	0.55	0.54	0.55	1600
5.0	0.71	0.74	0.72	1600
Accuracy	-	-	0.61	8000
Macro avg	0.61	0.61	0.61	8000
Weighted avg	0.61	0.61	0.61	8000

Cross-validation Scores:

Metric	Value
Logistic Regression Cross-validation scores	[0.61463816, 0.609375, 0.62072368, 0.59703947, 0.59835526]
Logistic Regression Mean Cross-validation score	0.6080

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.