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---
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dataset_info:
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features:
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- name: timestamp
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dtype: string
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- name: title
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dtype: string
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- name: description
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dtype: string
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- name: text
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dtype: string
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- name: market_direction
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dtype:
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class_label:
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names:
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'0': neutral
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'1': bearish
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'2': bullish
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- name: engagement_quality
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dtype:
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class_label:
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names:
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'0': neutral
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'1': liked
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'2': disliked
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- name: content_characteristics
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dtype:
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class_label:
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names:
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'0': neutral
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'1': important
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'2': lol
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- name: vote_counts
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struct:
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- name: bearish
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dtype: int32
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- name: bullish
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dtype: int32
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- name: liked
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dtype: int32
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- name: disliked
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dtype: int32
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- name: important
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dtype: int32
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- name: lol
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dtype: int32
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- name: total_votes
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dtype: int32
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- name: source_url
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dtype: string
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- name: url
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dtype: string
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- name: total_tokens
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dtype: int64
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splits:
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- name: train
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num_bytes: 22639057
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num_examples: 23301
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download_size: 12118601
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dataset_size: 22639057
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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language:
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- en
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tags:
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- DLT
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- Blockchain
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- Cryptocurrencies
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- Cryptocurrency
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- Bitcoin
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- Ethereum
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- XRP
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- Hedera
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pretty_name: Distributed Ledger Technology (DLT) / Blockchain Sentiment News
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size_categories:
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- 10K<n<100K
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---
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# DLT-Sentiment-News |
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## Dataset Description |
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### Dataset Summary |
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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. |
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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. |
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This dataset is part of the DLT-Corpus collection. For related datasets, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402 |
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### Supported Tasks |
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- **Sentiment Analysis**: Multi-dimensional sentiment classification for DLT and cryptocurrency content |
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- **Market Sentiment Studies**: Analyzing how cryptocurrency communities perceive market-related news |
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- **Content Quality Assessment**: Evaluating which content cryptocurrency users find valuable |
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- **Engagement Prediction**: Understanding what drives positive or negative community engagement |
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- **Model Evaluation**: Benchmarking domain-specific sentiment models |
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### Languages |
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English (en) |
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## Dataset Structure |
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### Data Fields |
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Each example in the dataset contains the following fields: |
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- **title**: Headline of the cryptocurrency news article |
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- **description**: Brief description or summary of the article |
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- **text**: Combined title and description text |
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- **timestamp**: Date and time when the article was posted |
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- **market_direction**: Sentiment about market direction (bullish, bearish, neutral) |
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- **engagement_quality**: Community assessment of content importance (important, lol, neutral) |
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- **content_characteristics**: User engagement type (liked, disliked, neutral) |
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- **vote_counts**: Detailed breakdown of votes for each sentiment category |
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- **total_votes**: Total number of community votes received |
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- **source_url**: URL of the original news source |
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- **url**: CryptoPanic URL for the article |
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- **total_tokens**: Total number of tokens in the text |
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### Label Distribution |
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The dataset includes three independent sentiment dimensions: |
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**Market Direction:** |
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- `bullish`: Positive outlook on market/price movement |
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- `bearish`: Negative outlook on market/price movement |
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- `neutral`: Balanced or unclear market direction |
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**Engagement Quality:** |
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- `important`: Content deemed significant by the community |
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- `lol`: Content considered humorous or not serious |
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- `neutral`: Standard content without strong quality signal |
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**Content Characteristics:** |
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- `liked`: Positively received by the community |
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- `disliked`: Negatively received by the community |
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- `neutral`: Mixed or neutral community reception |
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### Data Splits |
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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. |
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## Dataset Creation |
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### Curation Rationale |
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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: |
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- **Domain expertise**: Labels from active cryptocurrency users with market knowledge |
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- **Multi-dimensional sentiment**: Separate dimensions for market outlook, content quality, and engagement |
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- **Community consensus**: Aggregated opinions from multiple users rather than single annotators |
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- **Market context**: Sentiment tied to real cryptocurrency news and events |
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### Source Data |
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#### Data Collection |
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The dataset was collected from **CryptoPanic**, a cryptocurrency news aggregation platform where community members vote on news articles across multiple sentiment categories. |
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**Collection Details:** |
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- Data collected via CryptoPanic's free API between March and May 2025 |
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- Coverage period: January 2021 to May 2025 |
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- Only articles meeting minimum vote thresholds included (median minimum votes) |
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- All content is publicly available news headlines and descriptions |
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#### Data Processing |
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The collection and labeling process involved: |
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1. **Article retrieval**: Collecting news articles with community votes from CryptoPanic |
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2. **Vote normalization**: Calculating vote percentages by total engagement for each article |
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3. **Minimum threshold filtering**: Excluding articles with insufficient community engagement (below median votes) |
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4. **Percentile-based classification**: Using 25th and 75th percentiles as boundaries to assign labels |
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5. **Quality control**: Ensuring balanced representation across sentiment categories |
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### Annotations |
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#### Annotation Process |
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**Crowdsourced Community Voting:** |
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- Active cryptocurrency community members on CryptoPanic vote on news articles |
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- Users select from predefined sentiment categories for each dimension |
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- Votes reflect genuine community sentiment and domain expertise |
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**Label Assignment:** |
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- Percentile-based classification mitigates popularity bias |
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- Articles below 25th percentile labeled as negative category |
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- Articles above 75th percentile labeled as positive category |
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- Articles between percentiles labeled as neutral category |
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- Applied independently for each sentiment dimension |
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#### Who are the annotators? |
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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. |
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### Personal and Sensitive Information |
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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. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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This dataset can enable: |
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- **Positive impacts**: Better understanding of cryptocurrency community sentiment, improved market analysis tools, advancement of domain-specific NLP research, more accurate sentiment detection |
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- **Potential negative impacts**: Could be misused for market manipulation, creating misleading investment systems, or amplifying market volatility through automated trading |
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**Researchers should implement appropriate safeguards and ethical guidelines when working with this data.** |
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### Discussion of Biases |
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Potential biases include: |
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- **Platform bias**: Only reflects CryptoPanic users, not the entire cryptocurrency community |
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- **Language bias**: Only English-language news articles are included |
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- **Temporal bias**: More recent years may have different sentiment patterns than earlier periods |
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- **User bias**: Active voters may have different perspectives than passive readers |
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- **Source bias**: Certain news sources may be over-represented |
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- **Market condition bias**: Dataset may reflect specific market cycles (bull/bear markets) |
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- **Geographic bias**: English-speaking regions and news sources are over-represented |
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### Other Known Limitations |
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- **Temporal lag**: Not suitable for real-time sentiment analysis |
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- **Market volatility**: Sentiment may change rapidly after news publication |
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- **Vote manipulation**: Despite filters, coordinated voting cannot be completely ruled out |
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- **Context dependency**: Headlines lack full article context, which may affect sentiment interpretation |
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- **Evolving terminology**: Cryptocurrency terminology and memes evolve rapidly |
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- **Static snapshot**: Current version does not capture ongoing sentiment changes |
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## Additional Information |
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### Dataset Curators |
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Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu |
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### Licensing Information |
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**CC-BY-NC 4.0** (Creative Commons Attribution-NonCommercial 4.0 International) |
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This dataset is released under CC-BY-NC 4.0 for **research purposes only**. |
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**Key terms:** |
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- **Attribution required**: You must give appropriate credit to the dataset creators |
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- **Non-commercial use**: Commercial use is not permitted under this license |
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- **Academic research**: The dataset is intended for academic and non-profit research |
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**Legal basis:** |
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- Derived from publicly available CryptoPanic data with crowdsourced community annotations |
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- Data collected via CryptoPanic's free API between March and May 2025 |
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- 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 |
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For more information on CC-BY-NC 4.0, see: https://creativecommons.org/licenses/by-nc/4.0/ |
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### Acknowledgments |
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We thank the CryptoPanic platform and its community of users for making this dataset possible through their engagement and contributions to cryptocurrency news curation. |
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### Citation Information |
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```bibtex |
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@article{hernandez2025dlt-corpus, |
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title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain}, |
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author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua}, |
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year={2025} |
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} |
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``` |
