---
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>

<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

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.

<p align="center">
  <img width="80%" src="figures/fig3.png">
</p>

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.

This release includes multiple data splits with varying quality characteristics to support research in data quality assessment and curriculum learning.

## 2. Quality Metrics

### Comprehensive Quality Assessment

<div align="center">

| | 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 |

</div>

### Overall Quality Summary
The SentimentPro dataset demonstrates high quality across all evaluated dimensions, with particularly strong results in label accuracy and format compliance.

## 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"
}
```

## 4. Usage

```python
from datasets import load_dataset

dataset = load_dataset("username/SentimentPro-Dataset")
```

## 5. License
This dataset is licensed under the [Apache 2.0 License](LICENSE).

## 6. Contact
For questions or issues, please open an issue in this repository or contact data@sentimentpro.ai.