Added dataset card

README.md

@@ -1,66 +1,273 @@
----
-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-*
----
+---
+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}
+}
+```