README.md

---

dataset_info:
  features:
  - name: timestamp
    dtype: string
  - name: title
    dtype: string
  - name: description
    dtype: string
  - name: text
    dtype: string
  - name: market_direction
    dtype:
      class_label:
        names:
          '0': neutral
          '1': bearish
          '2': bullish
  - name: engagement_quality
    dtype:
      class_label:
        names:
          '0': neutral
          '1': liked
          '2': disliked
  - name: content_characteristics
    dtype:
      class_label:
        names:
          '0': neutral
          '1': important
          '2': lol
  - name: vote_counts
    struct:
    - name: bearish
      dtype: int32
    - name: bullish
      dtype: int32
    - name: liked
      dtype: int32
    - name: disliked
      dtype: int32
    - name: important
      dtype: int32
    - name: lol
      dtype: int32
  - name: total_votes
    dtype: int32
  - name: source_url
    dtype: string
  - name: url
    dtype: string
  - name: total_tokens
    dtype: int64
  splits:
  - name: train
    num_bytes: 22639057
    num_examples: 23301
  download_size: 12118601
  dataset_size: 22639057
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- en
tags:
- DLT
- Blockchain
- Cryptocurrencies
- Cryptocurrency
- Bitcoin
- Ethereum
- XRP
- Hedera
pretty_name: Distributed Ledger Technology (DLT) / Blockchain Sentiment News
size_categories:
- 10K<n<100K
---

# DLT-Sentiment-News

## Dataset Description

### Dataset Summary

DLT-Sentiment-News is a specialized sentiment analysis dataset for the Distributed Ledger Technology (DLT) domain. It addresses the lack of high-quality labeled data that captures domain-specific sentiment expressed by cryptocurrency community members.

The dataset contains **23,301 examples** with **1.85 million tokens** (average 79.51 tokens per example), spanning from **January 2021 to May 2025**. Each example includes cryptocurrency news headlines and descriptions with multi-dimensional sentiment labels crowdsourced from active community members on the CryptoPanic platform.

This dataset is part of the DLT-Corpus collection. For related datasets, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402

### Supported Tasks

- **Sentiment Analysis**: Multi-dimensional sentiment classification for DLT and cryptocurrency content
- **Market Sentiment Studies**: Analyzing how cryptocurrency communities perceive market-related news
- **Content Quality Assessment**: Evaluating which content cryptocurrency users find valuable
- **Engagement Prediction**: Understanding what drives positive or negative community engagement
- **Model Evaluation**: Benchmarking domain-specific sentiment models

### Languages

English (en)

## Dataset Structure

### Data Fields

Each example in the dataset contains the following fields:

- **title**: Headline of the cryptocurrency news article
- **description**: Brief description or summary of the article
- **text**: Combined title and description text
- **timestamp**: Date and time when the article was posted
- **market_direction**: Sentiment about market direction (bullish, bearish, neutral)
- **engagement_quality**: Community assessment of content importance (important, lol, neutral)
- **content_characteristics**: User engagement type (liked, disliked, neutral)
- **vote_counts**: Detailed breakdown of votes for each sentiment category
- **total_votes**: Total number of community votes received
- **source_url**: URL of the original news source
- **url**: CryptoPanic URL for the article
- **total_tokens**: Total number of tokens in the text

### Label Distribution

The dataset includes three independent sentiment dimensions:

**Market Direction:**
- `bullish`: Positive outlook on market/price movement
- `bearish`: Negative outlook on market/price movement  
- `neutral`: Balanced or unclear market direction

**Engagement Quality:**
- `important`: Content deemed significant by the community
- `lol`: Content considered humorous or not serious
- `neutral`: Standard content without strong quality signal

**Content Characteristics:**
- `liked`: Positively received by the community
- `disliked`: Negatively received by the community
- `neutral`: Mixed or neutral community reception

### Data Splits

This is a single corpus without predefined splits. Users should create their own train/validation/test splits based on their specific research needs. Consider temporal splits to avoid data leakage when studying market trends.

## Dataset Creation

### Curation Rationale

DLT-Sentiment-News was created to support sentiment analysis research in the DLT domain with data that reflects authentic community perspectives. Unlike general sentiment datasets, this captures:

- **Domain expertise**: Labels from active cryptocurrency users with market knowledge
- **Multi-dimensional sentiment**: Separate dimensions for market outlook, content quality, and engagement
- **Community consensus**: Aggregated opinions from multiple users rather than single annotators
- **Market context**: Sentiment tied to real cryptocurrency news and events

### Source Data

#### Data Collection

The dataset was collected from **CryptoPanic**, a cryptocurrency news aggregation platform where community members vote on news articles across multiple sentiment categories.

**Collection Details:**
- Data collected via CryptoPanic's free API between March and May 2025
- Coverage period: January 2021 to May 2025
- Only articles meeting minimum vote thresholds included (median minimum votes)
- All content is publicly available news headlines and descriptions

#### Data Processing

The collection and labeling process involved:

1. **Article retrieval**: Collecting news articles with community votes from CryptoPanic
2. **Vote normalization**: Calculating vote percentages by total engagement for each article
3. **Minimum threshold filtering**: Excluding articles with insufficient community engagement (below median votes)
4. **Percentile-based classification**: Using 25th and 75th percentiles as boundaries to assign labels
5. **Quality control**: Ensuring balanced representation across sentiment categories

### Annotations

#### Annotation Process

**Crowdsourced Community Voting:**
- Active cryptocurrency community members on CryptoPanic vote on news articles
- Users select from predefined sentiment categories for each dimension
- Votes reflect genuine community sentiment and domain expertise

**Label Assignment:**
- Percentile-based classification mitigates popularity bias
- Articles below 25th percentile labeled as negative category
- Articles above 75th percentile labeled as positive category  
- Articles between percentiles labeled as neutral category
- Applied independently for each sentiment dimension

#### Who are the annotators?

Active cryptocurrency community members on the CryptoPanic platform. These annotators possess domain expertise and genuine interest in DLT/cryptocurrency news, providing more relevant sentiment labels than general crowdworkers.

### Personal and Sensitive Information

This dataset contains only publicly available cryptocurrency news headlines and descriptions. No personal or confidential data is included. Individual voter information is not included - only aggregated vote counts and percentages are retained.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset can enable:

- **Positive impacts**: Better understanding of cryptocurrency community sentiment, improved market analysis tools, advancement of domain-specific NLP research, more accurate sentiment detection
- **Potential negative impacts**: Could be misused for market manipulation, creating misleading investment systems, or amplifying market volatility through automated trading

**Researchers should implement appropriate safeguards and ethical guidelines when working with this data.**

### Discussion of Biases

Potential biases include:

- **Platform bias**: Only reflects CryptoPanic users, not the entire cryptocurrency community
- **Language bias**: Only English-language news articles are included
- **Temporal bias**: More recent years may have different sentiment patterns than earlier periods
- **User bias**: Active voters may have different perspectives than passive readers
- **Source bias**: Certain news sources may be over-represented
- **Market condition bias**: Dataset may reflect specific market cycles (bull/bear markets)
- **Geographic bias**: English-speaking regions and news sources are over-represented

### Other Known Limitations

- **Temporal lag**: Not suitable for real-time sentiment analysis
- **Market volatility**: Sentiment may change rapidly after news publication
- **Vote manipulation**: Despite filters, coordinated voting cannot be completely ruled out
- **Context dependency**: Headlines lack full article context, which may affect sentiment interpretation
- **Evolving terminology**: Cryptocurrency terminology and memes evolve rapidly
- **Static snapshot**: Current version does not capture ongoing sentiment changes

## Additional Information

### Dataset Curators

Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu

### Licensing Information

**CC-BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International)

This dataset is released under CC-BY-NC 4.0 for **research purposes only**.

**Key terms:**
- **Attribution required**: You must give appropriate credit to the dataset creators
- **Non-commercial use**: Commercial use is not permitted under this license
- **Academic research**: The dataset is intended for academic and non-profit research

**Legal basis:** 
- Derived from publicly available CryptoPanic data with crowdsourced community annotations
- Data collected via CryptoPanic's free API between March and May 2025
- To the best of our knowledge, the Terms of Service at the time of collection (cryptopanic.com/terms/) contained no restrictions on academic research use or redistribution

For more information on CC-BY-NC 4.0, see: https://creativecommons.org/licenses/by-nc/4.0/


### Acknowledgments

We thank the CryptoPanic platform and its community of users for making this dataset possible through their engagement and contributions to cryptocurrency news curation.

### Citation Information

```bibtex
@article{hernandez2025dlt-corpus,
  title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
  author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua},
  year={2025}
}
```