text stringclasses 3
values | sentiment stringclasses 3
values | source stringclasses 2
values | asset_type stringclasses 3
values |
|---|---|---|---|
Bitcoin rallies above $60,000 amid institutional buying. | positive | news | crypto |
Federal Reserve signals possible interest rate hike. | negative | news | economy |
Apple stock remains stable during quiet trading session. | neutral | social | stock |
YAML Metadata Warning:The task_categories "sentiment-analysis" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
💰 Financial Sentiment Analysis Dataset
A dataset containing financial news headlines and social media posts labeled with sentiment.
Designed for:
- Financial NLP research
- Market sentiment analysis
- Trading signal modeling
📊 Dataset Statistics
- Train: 30,000 samples
- Validation: 5,000 samples
- Test: 5,000 samples
- Total: 40,000 samples
📄 Data Format
{ "text": "Tesla stock surges after strong earnings report.", "sentiment": "positive", "source": "news", "asset_type": "stock" }
Sentiment Labels:
- positive
- neutral
- negative
🎯 Intended Use
- Sentiment classification models
- Financial forecasting pipelines
- NLP benchmarking
⚠️ Limitations
- English only
- Short-form text focused
- Downloads last month
- 20