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--- |
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license: mit |
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datasets: |
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- nhull/tripadvisor-split-dataset-v2 |
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language: |
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- en |
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pipeline_tag: text-classification |
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tags: |
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- sentiment-analysis |
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- random-forest |
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- text-classification |
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- hotel-reviews |
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- tripadvisor |
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- nlp |
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--- |
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# Random Forest Sentiment Analysis Model |
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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. |
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## Model Details |
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- **Model Type**: Random Forest |
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- **Task**: Sentiment Analysis |
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- **Input**: A hotel review (text) |
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- **Output**: Sentiment rating (1-5 stars) |
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- **Dataset Used**: TripAdvisor sentiment dataset (balanced labels) |
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## Intended Use |
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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. |
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## How to Use the Model |
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1. **Install the required dependencies**: |
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```bash |
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pip install joblib |
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``` |
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2. **Download and load the model**: |
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You can download the model from Hugging Face and use it to predict sentiment. |
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Example code to download and use the model: |
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```python |
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from huggingface_hub import hf_hub_download |
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import joblib |
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# Download model from Hugging Face |
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model_path = hf_hub_download(repo_id="your-username/random-forest-model", filename="random_forest_model.joblib") |
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# Load the model |
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model = joblib.load(model_path) |
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# Predict sentiment of a review |
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def predict_sentiment(review): |
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return model.predict([review])[0] |
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review = "This hotel was fantastic. The service was great and the room was clean." |
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print(f"Predicted sentiment: {predict_sentiment(review)}") |
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``` |
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3. **The model will return a sentiment rating** between 1 and 5 stars, where: |
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- 1: Very bad |
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- 2: Bad |
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- 3: Neutral |
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- 4: Good |
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- 5: Very good |
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## Model Evaluation |
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- **Test Accuracy**: 55.28% on the test set. |
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- **Classification Report** (Test Set): |
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| Label | Precision | Recall | F1-score | Support | |
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|-------|-----------|--------|----------|---------| |
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| 1.0 | 0.62 | 0.78 | 0.69 | 1600 | |
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| 2.0 | 0.48 | 0.38 | 0.42 | 1600 | |
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| 3.0 | 0.49 | 0.40 | 0.44 | 1600 | |
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| 4.0 | 0.49 | 0.46 | 0.48 | 1600 | |
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| 5.0 | 0.63 | 0.74 | 0.68 | 1600 | |
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| **Accuracy** | - | - | **0.55** | 8000 | |
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| **Macro avg** | 0.54 | 0.55 | 0.54 | 8000 | |
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| **Weighted avg** | 0.54 | 0.55 | 0.54 | 8000 | |
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### Cross-validation Scores: |
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| Metric | Value | |
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|-------------------------------------|--------------------------------------------| |
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| **Random Forest Cross-validation scores** | [0.54983553, 0.55164474, 0.55805921, 0.55657895, 0.54424342] | |
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| **Random Forest Mean Cross-validation score** | 0.5521 | |
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## Limitations |
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- The model performs well on extreme ratings (1 and 5 stars) but struggles with intermediate ratings (2, 3, and 4 stars). |
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- The model was trained on the **TripAdvisor** dataset and may not generalize well to reviews from other sources or domains. |
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- The model does not handle aspects like sarcasm or humor well, and shorter reviews may lead to less accurate predictions. |
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