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
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. 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.