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---
license: apache-2.0
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)
- **Trained Dataset**: [nhull/tripadvisor-split-dataset-v2](https://huggingface.co/datasets/nhull/tripadvisor-split-dataset-v2)
## 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.
---
**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
---
### Dataset
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:
- **Training Set**: 30,400 reviews
- **Validation Set**: 1,600 reviews
- **Test Set**: 8,000 reviews
All splits are balanced across five sentiment labels.
---
### Test Performance
Model predicts too high on average by `0.44`.
- **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 |
---
## 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. |