---
license: apache-2.0
language:
- vi
pretty_name: UTS2017 Bank Dataset
tags:
- text
- vietnamese
- banking
- finance
- nlp
- sentiment-analysis
- aspect-based-sentiment-analysis
task_categories:
- text-classification
size_categories:
- 1K<n<10K
configs:
- config_name: classification
  data_files:
  - split: train
    path: data/classification/train.jsonl
  - split: test
    path: data/classification/test.jsonl
- config_name: sentiment
  data_files:
  - split: train
    path: data/sentiment/train.jsonl
  - split: test
    path: data/sentiment/test.jsonl
- config_name: aspect_sentiment
  data_files:
  - split: train
    path: data/aspect_sentiment/train.jsonl
  - split: test
    path: data/aspect_sentiment/test.jsonl
---

# UTS2017_Bank Dataset

## Dataset Description

### Dataset Summary

The UTS2017_Bank dataset is a comprehensive Vietnamese banking domain dataset containing customer feedback and reviews about banking services. It contains **2,471 annotated examples** (1,977 train, 494 test) with both aspect labels and sentiment annotations. The dataset supports multiple NLP tasks including aspect classification, sentiment analysis, and aspect-based sentiment analysis in the Vietnamese banking sector.

### Supported Tasks

The dataset is provided in three subsets for different tasks:

1. **Classification** (`classification`): Banking aspect classification
   - 14 aspect categories (CUSTOMER_SUPPORT, TRADEMARK, LOAN, etc.)
   - Train: 1,977 examples | Test: 494 examples

2. **Sentiment Analysis** (`sentiment`): Overall sentiment classification
   - 3 sentiment classes: positive (61.3%), negative (37.6%), neutral (1.2%)
   - Train: 1,977 examples | Test: 494 examples

3. **Aspect-Based Sentiment** (`aspect_sentiment`): Fine-grained aspect-sentiment pairs
   - Multi-aspect support (1.5% examples have multiple aspects)
   - Train: 1,977 examples | Test: 494 examples

### Languages

The dataset is exclusively in Vietnamese (`vi`).

## Dataset Structure

### Data Instances

Each subset has a different structure:

**Classification subset:**
```json
{
  "text": "Hotline khó gọi quá gọi mãi ko thưa máy à",
  "label": "CUSTOMER_SUPPORT"
}
```

**Sentiment subset:**
```json
{
  "text": "Dịch vụ tiện dụng quá!",
  "sentiment": "positive"
}
```

**Aspect-Sentiment subset:**
```json
{
  "text": "Mình xài cái thể VISA của BIDV hạn mức 100tr...",
  "aspects": [
    {"aspect": "CARD", "sentiment": "negative"},
    {"aspect": "CUSTOMER_SUPPORT", "sentiment": "negative"}
  ]
}
```

### Data Fields

- **Classification subset:**
  - `text` (string): Customer feedback text in Vietnamese
  - `label` (string): Banking aspect category

- **Sentiment subset:**
  - `text` (string): Customer feedback text in Vietnamese
  - `sentiment` (string): Overall sentiment (positive/negative/neutral)

- **Aspect-Sentiment subset:**
  - `text` (string): Customer feedback text in Vietnamese
  - `aspects` (list): List of aspect-sentiment pairs

### Aspect Categories

The dataset contains 14 banking aspect categories:

| Aspect | Train Count | Test Count | Description |
|--------|------------|------------|-------------|
| CUSTOMER_SUPPORT | 774 (39.2%) | 338 (68.4%) | Customer service quality |
| TRADEMARK | 699 (35.4%) | 41 (8.3%) | Bank brand and reputation |
| LOAN | 74 (3.7%) | 3 (0.6%) | Loan services |
| INTERNET_BANKING | 70 (3.5%) | 32 (6.5%) | Online banking services |
| CARD | 66 (3.3%) | 44 (8.9%) | Credit/debit card services |
| INTEREST_RATE | 60 (3.0%) | 6 (1.2%) | Interest rates |
| PROMOTION | 53 (2.7%) | 9 (1.8%) | Promotional offers |
| OTHER | 69 (3.5%) | 12 (2.4%) | Other topics |
| DISCOUNT | 41 (2.1%) | 2 (0.4%) | Discounts and benefits |
| MONEY_TRANSFER | 34 (1.7%) | 2 (0.4%) | Money transfer services |
| PAYMENT | 15 (0.8%) | 2 (0.4%) | Payment services |
| SAVING | 13 (0.7%) | 3 (0.6%) | Savings accounts |
| ACCOUNT | 5 (0.3%) | 0 (0%) | Account management |
| SECURITY | 4 (0.2%) | 0 (0%) | Security concerns |

### Data Statistics

- **Text Length:**
  - Train: Average 23.8 words (range 1-816)
  - Test: Average 30.6 words (range 1-411)

- **Sentiment Distribution:**
  - Train: Positive (1,211), Negative (743), Neutral (23)
  - Test: Negative (301), Positive (185), Neutral (8)

- **Multi-aspect Examples:**
  - Train: 29/1,977 examples (1.5%)
  - Test: 2/494 examples (0.4%)

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load classification subset
dataset_clf = load_dataset("undertheseanlp/UTS2017_Bank", "classification")

# Load sentiment subset
dataset_sent = load_dataset("undertheseanlp/UTS2017_Bank", "sentiment")

# Load aspect-sentiment subset
dataset_aspect = load_dataset("undertheseanlp/UTS2017_Bank", "aspect_sentiment")
```

### Data Preprocessing

Scripts are available in the repository:

```bash
# Process raw data into three subsets
python preprocess.py

# Generate dataset statistics
python stats.py

# Validate dataset loading from HuggingFace Hub
python validate.py
```

## Dataset Creation

### Curation Rationale

This dataset addresses the lack of Vietnamese NLP resources in the banking sector, supporting the development of specialized models for:
- Customer feedback analysis
- Service quality monitoring
- Banking chatbots and virtual assistants
- Financial sentiment analysis

### Source Data

The dataset contains real customer feedback and reviews about Vietnamese banking services, collected from public sources and banking communications. All texts have been anonymized to remove personal information.

### Annotations

The dataset includes:
- 14 banking-specific aspect categories
- 3-class sentiment labels (positive, negative, neutral)
- Support for multi-aspect annotations (1.5% of examples)

### Personal and Sensitive Information

All personal information, account numbers, and sensitive financial data have been removed. The dataset contains only general banking terminology and anonymized feedback.

## Considerations for Using the Data

### Social Impact

This dataset enables:
- Better understanding of customer needs in Vietnamese banking
- Improved customer service through automated analysis
- Enhanced financial inclusion through better language support

### Discussion of Biases

- **Language style**: Primarily informal customer feedback language
- **Sentiment imbalance**: More positive examples in training set
- **Aspect distribution**: Heavy skew towards CUSTOMER_SUPPORT and TRADEMARK

### Known Limitations

- Limited neutral sentiment examples (1-2%)
- Aspect distribution varies between train and test sets
- Multi-aspect examples are rare (1.5%)
- Domain-specific to banking sector

## Additional Information

### Dataset Curators

Created and maintained by the UnderTheSea NLP team, focusing on Vietnamese NLP resources and tools development.

### Licensing Information

Released under the Apache 2.0 License.

### Citation Information

```bibtex
@dataset{uts2017_bank,
  title={UTS2017_Bank: A Vietnamese Banking Domain Dataset for Aspect-Based Sentiment Analysis},
  author={UnderTheSea NLP},
  year={2017},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/undertheseanlp/UTS2017_Bank}
}
```

### Contributions

Thanks to the UnderTheSea NLP community for creating and maintaining this dataset.

## Contact

For questions or contributions:
- Open an issue on the [dataset repository](https://huggingface.co/datasets/undertheseanlp/UTS2017_Bank/discussions)
- Visit [UnderTheSea NLP](https://github.com/undertheseanlp)

## Updates and Versions

- **Version 1.0.0** (Current): Initial release with 2,471 annotated banking feedback examples in three task-specific subsets