Hierarchical text classification Dataset
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Hierarchical text classification
- Labeling type: Product Category
- Data Format: Text
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Use cases of research datasets
About Dataset
1) Data Introduction
• Hierarchical text focuses on hierarchical text classification, featuring structured classes at three levels. The dataset includes product reviews, scores, and categories, providing a comprehensive resource for exploring various text classification approaches.2) Data Utilization
(1) Hierarchical text data has characteristics that: • The dataset contains detailed product reviews along with scores, categories, and user feedback. This allows for in-depth analysis and hierarchical classification of text data, useful for understanding customer opinions and product categorizations. (2) Hierarchical text data can be used to: • E-commerce Analysis: Helps in understanding customer sentiment, improving product recommendations, and optimizing marketing strategies. • Research: Supports academic studies and development of advanced text classification models and natural language processing techniques.Meta Data
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Data Samples
Utility
| Downstream Classification (▲) | MMD (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
MMD (Maximum Mean Discrepancy) is a metric used to assess the similarity between two probability distributions. It is commonly used to compare generated data with real data. High MMD score: A score above 0.05 indicates that the two distributions may differ. Low MMD score: Indicates that the generated data is similar to the real data. A score close to 0 is preferable, and a score below 0.01 suggests that the two data distributions are nearly indistinguishable.
Privacy
| ROUGE (▼) | BERTScore (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The closer to zero or the lower the value, the better (▼)
Structural Similarity
Inference risk measures the risk of inferring sensitive information about the original data from synthetic data. It evaluates the likelihood of extracting original data information from synthetic data and is calculated by comparing the distance between synthetic and original data. High Duplication Rate: Indicates lower data diversity and potential quality issues, which can reduce the reliability of analysis and models. Interpretation: A lower risk value means that synthetic data is less likely to infer sensitive information, indicating higher data security.
Perceptual Similarity
BERTScore is a metric used to evaluate the semantic similarity between two texts, utilizing deep learning models like BERT (Bidirectional Encoder Representations from Transformers) to capture the meaning of the texts.. High BERTScore: If the value is 0.9 or higher, it indicates that the generated text is almost semantically identical to the real text. This can increase the risk of sensitive information leakage. Low BERTScore: If the value is 0.6 or lower, it indicates lower similarity and a reduced risk of leakage.
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