| --- |
| license: apache-2.0 |
| task_categories: |
| - text-classification |
| - question-answering |
| --- |
| # SentimentPro Dataset |
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| <div align="center"> |
| <img src="figures/fig1.png" width="60%" alt="SentimentPro Dataset" /> |
| </div> |
| <hr> |
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| <div align="center" style="line-height: 1;"> |
| <a href="LICENSE" style="margin: 2px;"> |
| <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
| ## 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"> |
| <img width="80%" src="figures/fig3.png"> |
| </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 | |
| |---|---|---|---|---|---| |
| | **Data Integrity** | Completeness | 0.820 | 0.855 | 0.870 | 0.907 | |
| | | Consistency | 0.785 | 0.812 | 0.835 | 0.892 | |
| | | Accuracy | 0.910 | 0.925 | 0.940 | 0.977 | |
| | **Temporal Quality** | Timeliness | 0.750 | 0.780 | 0.805 | 0.868 | |
| | | Uniqueness | 0.890 | 0.915 | 0.930 | 0.968 | |
| | | Validity | 0.865 | 0.880 | 0.900 | 0.950 | |
| | **Content Quality** | Coverage | 0.720 | 0.755 | 0.780 | 0.843 | |
| | | Diversity | 0.695 | 0.730 | 0.760 | 0.835 | |
| | | Relevance | 0.840 | 0.865 | 0.885 | 0.935 | |
| | | Balance | 0.775 | 0.805 | 0.830 | 0.892 | |
| | **Technical Quality** | Noise Level | 0.680 | 0.720 | 0.755 | 0.843 | |
| | | Label Quality | 0.905 | 0.920 | 0.935 | 0.973 | |
| | | Text Quality | 0.830 | 0.855 | 0.875 | 0.925 | |
| | | Format Compliance | 0.945 | 0.960 | 0.970 | 0.995 | |
| | | Schema Validation | 0.990 | 0.992 | 0.995 | 0.999 | |
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| </div> |
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| ### Overall Quality Summary |
| 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 |
| |
| ``` |
| data/ |
| ├── train.jsonl # Training examples |
| ├── validation.jsonl # Validation examples |
| └── test.jsonl # Test examples (labels withheld) |
| ``` |
| |
| Each example follows this schema: |
| ```json |
| { |
| "id": "unique_identifier", |
| "text": "The input text content", |
| "label": "positive|negative|neutral", |
| "domain": "social_media|reviews|news", |
| "language": "en|es|fr|de|zh" |
| } |
| ``` |
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| ## 4. Usage |
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| ```python |
| from datasets import load_dataset |
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| dataset = load_dataset("username/SentimentPro-Dataset") |
| ``` |
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| ## 5. License |
| This dataset is licensed under the [Apache 2.0 License](LICENSE). |
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| ## 6. Contact |
| For questions or issues, please open an issue in this repository or contact data@sentimentpro.ai. |
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