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--- |
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license: apache-2.0 |
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task_categories: |
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- text-classification |
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- question-answering |
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--- |
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# SentimentPro Dataset |
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<!-- markdownlint-disable first-line-h1 --> |
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<!-- markdownlint-disable html --> |
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<!-- markdownlint-disable no-duplicate-header --> |
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<div align="center"> |
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<img src="figures/fig1.png" width="60%" alt="SentimentPro Dataset" /> |
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</div> |
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<hr> |
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<div align="center" style="line-height: 1;"> |
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<a href="LICENSE" style="margin: 2px;"> |
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<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
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</a> |
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</div> |
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## 1. Introduction |
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SentimentPro is a comprehensive multilingual sentiment analysis dataset designed for training and evaluating sentiment classification models. The dataset includes labeled examples from multiple domains including social media, product reviews, and news articles. |
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<p align="center"> |
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<img width="80%" src="figures/fig3.png"> |
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</p> |
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The dataset was curated using advanced filtering techniques to ensure high quality annotations. Each example has been validated by at least 3 independent annotators with high inter-annotator agreement scores. |
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This release includes multiple data splits with varying quality characteristics to support research in data quality assessment and curriculum learning. |
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## 2. Quality Metrics |
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### Comprehensive Quality Assessment |
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<div align="center"> |
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| | Quality Metric | Split_v1 | Split_v3 | Split_v5 | SentimentPro-Best | |
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|---|---|---|---|---|---| |
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| **Data Integrity** | Completeness | 0.820 | 0.855 | 0.870 | 0.907 | |
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| | Consistency | 0.785 | 0.812 | 0.835 | 0.892 | |
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| | Accuracy | 0.910 | 0.925 | 0.940 | 0.977 | |
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| **Temporal Quality** | Timeliness | 0.750 | 0.780 | 0.805 | 0.868 | |
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| | Uniqueness | 0.890 | 0.915 | 0.930 | 0.968 | |
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| | Validity | 0.865 | 0.880 | 0.900 | 0.950 | |
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| **Content Quality** | Coverage | 0.720 | 0.755 | 0.780 | 0.843 | |
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| | Diversity | 0.695 | 0.730 | 0.760 | 0.835 | |
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| | Relevance | 0.840 | 0.865 | 0.885 | 0.935 | |
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| | Balance | 0.775 | 0.805 | 0.830 | 0.892 | |
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| **Technical Quality** | Noise Level | 0.680 | 0.720 | 0.755 | 0.843 | |
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| | Label Quality | 0.905 | 0.920 | 0.935 | 0.973 | |
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| | Text Quality | 0.830 | 0.855 | 0.875 | 0.925 | |
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| | Format Compliance | 0.945 | 0.960 | 0.970 | 0.995 | |
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| | Schema Validation | 0.990 | 0.992 | 0.995 | 0.999 | |
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</div> |
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### Overall Quality Summary |
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The SentimentPro dataset demonstrates high quality across all evaluated dimensions, with particularly strong results in label accuracy and format compliance. |
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## 3. Dataset Structure |
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``` |
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data/ |
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├── train.jsonl # Training examples |
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├── validation.jsonl # Validation examples |
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└── test.jsonl # Test examples (labels withheld) |
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``` |
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Each example follows this schema: |
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```json |
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{ |
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"id": "unique_identifier", |
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"text": "The input text content", |
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"label": "positive|negative|neutral", |
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"domain": "social_media|reviews|news", |
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"language": "en|es|fr|de|zh" |
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} |
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``` |
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## 4. Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("username/SentimentPro-Dataset") |
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``` |
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## 5. License |
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This dataset is licensed under the [Apache 2.0 License](LICENSE). |
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## 6. Contact |
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For questions or issues, please open an issue in this repository or contact data@sentimentpro.ai. |
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