README.md

---
license: mit
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': very_negative
          '1': negative
          '2': neutral
          '3': positive
          '4': very_positive
  splits:
  - name: train
    num_examples: 10820
  - name: validation
    num_examples: 1352
  - name: test
    num_examples: 1353
  dataset_size: 13525
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.csv
  - split: validation
    path: data/val.csv
  - split: test
    path: data/test.csv
task_categories:
- text-classification
language:
- fa
tags:
- sentiment
- review
pretty_name: SentiPers
size_categories:
- 1K<n<10K
---

# SentiPers Dataset

## Dataset Description

SentiPers is a Persian sentiment analysis dataset containing text samples labeled with sentiment polarity scores ranging from -2 (very negative) to 2 (very positive).

**Original Repository:** https://github.com/phosseini/SentiPers

**Fork:** https://github.com/k-forghani/SentiPers

## Dataset Structure

### Data Splits

The dataset is split into three subsets with fixed random seed (42) for reproducibility:

| Split | Size | Percentage |
|-------|------|------------|
| Train | 10,820 | 80% |
| Validation | 1,352 | 10% |
| Test | 1,353 | 10% |

### Data Fields

- `text`: The input text in Persian
- `label`: Sentiment polarity label (integer from 0 to 4)

### Label Mapping

The original polarity scores are mapped to integer labels as follows:

| Original Polarity | Label ID | Sentiment |
|------------------|----------|-----------|
| -2 | 0 | Very Negative |
| -1 | 1 | Negative |
| 0 | 2 | Neutral |
| 1 | 3 | Positive |
| 2 | 4 | Very Positive |

## Dataset Creation

The dataset is preprocessed from `sentipers.xlsx` with the following steps:

1. Extract text and polarity columns
2. Remove duplicate texts (keeping first occurrence)
3. Map polarity scores to integer labels
4. Perform stratified splitting to maintain label distribution across splits

## Loading the Dataset

```python
import pandas as pd

train_df = pd.read_csv('data/train.csv')
val_df = pd.read_csv('data/val.csv')
test_df = pd.read_csv('data/test.csv')
```

## Preprocessing Script

Run `preprocess_data.py` to regenerate the splits from the source file:

```bash
python preprocess_data.py
```

## Citation

If you use this dataset, please cite the original SentiPers repository:

**Original:** https://github.com/phosseini/SentiPers

**Fork:** https://github.com/k-forghani/SentiPers