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Datasets:
undertheseanlp
/
UTS2017_Bank

Tasks:
Text Classification
Modalities:
Text
Formats:
json
Languages:
Vietnamese
Size:
1K - 10K
Tags:
text
vietnamese
banking
finance
nlp
sentiment-analysis
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metadata 
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:

{
  "text": "Hotline khó gọi quá gọi mãi ko thưa máy à",
  "label": "CUSTOMER_SUPPORT"
}

Sentiment subset:

{
  "text": "Dịch vụ tiện dụng quá!",
  "sentiment": "positive"
}

Aspect-Sentiment subset:

{
  "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 

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:

# 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 

@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:

Updates and Versions

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