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license: apache-2.0 |
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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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- logistic-regression |
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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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# Logistic Regression Sentiment Analysis Model |
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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. |
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## Model Details |
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- **Model Type**: Logistic Regression |
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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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- **Trained Dataset**: [nhull/tripadvisor-split-dataset-v2](https://huggingface.co/datasets/nhull/tripadvisor-split-dataset-v2) |
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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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**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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### Dataset |
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The dataset used for training, validation, and testing is [nhull/tripadvisor-split-dataset-v2](https://huggingface.co/datasets/nhull/tripadvisor-split-dataset-v2). It consists of: |
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- **Training Set**: 30,400 reviews |
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- **Validation Set**: 1,600 reviews |
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- **Test Set**: 8,000 reviews |
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All splits are balanced across five sentiment labels. |
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### Test Performance |
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Model predicts too high on average by `0.44`. |
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- **Test Accuracy**: 61.05% on the test set. |
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- **Classification Report**: |
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| Label | Precision | Recall | F1-score | Support | |
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|-------|-----------|--------|----------|---------| |
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| 1.0 | 0.70 | 0.73 | 0.71 | 1600 | |
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| 2.0 | 0.52 | 0.50 | 0.51 | 1600 | |
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| 3.0 | 0.57 | 0.54 | 0.55 | 1600 | |
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| 4.0 | 0.55 | 0.54 | 0.55 | 1600 | |
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| 5.0 | 0.71 | 0.74 | 0.72 | 1600 | |
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| **Accuracy** | - | - | **0.61** | 8000 | |
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| **Macro avg** | 0.61 | 0.61 | 0.61 | 8000 | |
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| **Weighted avg** | 0.61 | 0.61 | 0.61 | 8000 | |
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- **Confusion Matrix**: |
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| True \\ Predicted | 1 | 2 | 3 | 4 | 5 | |
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|-------------------|-------|-------|-------|-------|-------| |
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| 1 | 1165 | 384 | 41 | 3 | 7 | |
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| 2 | 432 | 805 | 315 | 31 | 17 | |
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| 3 | 61 | 314 | 857 | 311 | 57 | |
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| 4 | 3 | 48 | 264 | 870 | 415 | |
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| 5 | 6 | 10 | 32 | 365 | 1187 | |
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## Files Included |
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- **`validation_results_log_regression.csv`**: Contains correctly classified reviews with their real and predicted labels. |
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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. |
