Upload README.md

README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-regression
+- text-classification
+language:
+- en
+tags:
+- psychology
+- emotion
+- distress
+- misery
+- sentiment-analysis
+- regression
+size_categories:
+- 100<n<1K
+---
+
+# Misery Index Dataset
+
+## Dataset Description
+
+The Misery Index Dataset comprises 516 textual descriptions of real-world or imagined scenarios, each annotated with a corresponding misery score on a continuous scale from 0 (no misery) to 100 (extreme misery). These misery ratings represent subjective estimates of emotional distress associated with each event.
+
+## Dataset Summary
+
+This dataset is designed for research in emotional AI, sentiment analysis, and psychological modeling. It enables researchers to develop and evaluate models that can predict human emotional responses to various life situations with fine-grained precision.
+
+### Key Features:
+- **516 scenarios** with diverse emotional contexts
+- **Continuous scale (0-100)** for precise misery measurement
+- **Minimal preprocessing** to preserve emotional texture
+- **Balanced distribution** across misery levels
+- **Categorized events** for structured analysis
+
+## Dataset Structure
+
+### Data Fields
+
+- `Ep #` (string): Source episode identifier (e.g., "1x01", "2x03")
+- `Misery` (string): A short English-language description of a miserable situation
+- `Score` (int): Numeric label indicating misery level (0-100 scale)
+- `VNTO` (string): Content type flag (T=Text, V=Video, N=News, O=Other, P=Punishment)
+- `Reward` (int): Reward value from original game show context (0-15000)
+- `Win` (string): Win/loss indicator (y/n)
+- `Comments` (string): Additional comments, notes, or source information
+- `question_tag` (string): Question categorization tag
+- `level` (string): Difficulty or context level
+
+### Data Splits
+
+The dataset contains a single train split with 516 examples.
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("path/to/misery-index")
+print(dataset["train"][0])
+# Output example:
+# {
+#   'Ep #': '1x01',
+#   'Misery': 'You Send a Nude Selfie to HR by mistake', 
+#   'Score': 70,
+#   'VNTO': 'T',
+#   'Reward': 0,
+#   'Win': '',
+#   'Comments': '',
+#   'question_tag': '1_base',
+#   'level': ''
+# }
+```
+
+## Dataset Statistics
+
+- **Total examples**: 516
+- **Mean misery score**: 56.45
+- **Standard deviation**: 17.59
+- **Score range**: 11-100
+- **Percentiles**: 
+  - 25th: 43
+  - 50th: 56  
+  - 75th: 69
+
+### Category Distribution
+
+1. **Other/Miscellaneous**: 26.4%
+2. **Family or Relationship Issues**: 16.3%
+3. **Accidents or Mishaps**: 15.3%
+4. **Medical Emergencies**: ~10%
+5. **Embarrassment**: ~8%
+6. **Physical Injury**: ~7%
+7. **Animal-related Incidents**: ~6%
+8. **Crime or Legal Trouble**: <5%
+9. **Professional/Work-related**: <5%
+10. **Gross/Disgusting Events**: <5%
+
+## Data Sources
+
+The data was aggregated from three primary sources:
+
+1. **Misery Index blog** curated by Bobby MGSK
+2. **Jericho Blog** consolidated dataset
+3. **Associated Google Spreadsheet** with structured entries
+
+Original sources:
+- [Bobby MGSK's Misery Index Blog](https://bobbymgsk.wordpress.com/category/the-misery-index/)
+- [Jericho Blog Compilation](https://jericho.blog/2021/02/03/the-misery-index-data/)
+- [Google Spreadsheet](https://docs.google.com/spreadsheets/d/151WjFwDdhIURf48subj6SDOdra0XVIEo0xulnBMMfRo/edit#gid=1169151367)
+
+## Usage Examples
+
+### Basic Loading and Exploration
+
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load the dataset
+dataset = load_dataset("path/to/misery-index")
+
+# Convert to pandas for analysis
+df = dataset["train"].to_pandas()
+
+# Basic statistics
+print(f"Dataset size: {len(df)}")
+print(f"Average misery score: {df['Score'].mean():.2f}")
+print(f"Score range: {df['Score'].min()}-{df['Score'].max()}")
+
+# Sample scenarios by misery level
+print("\nLow misery scenarios:")
+print(df[df['Score'] < 30]['Misery'].head())
+
+print("\nHigh misery scenarios:")
+print(df[df['Score'] > 80]['Misery'].head())
+```
+
+### Regression Task
+
+```python
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
+# Prepare features and targets
+X = df['Misery'].values
+y = df['Score'].values
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Vectorize text
+vectorizer = TfidfVectorizer(max_features=1000, stop_words='english')
+X_train_vec = vectorizer.fit_transform(X_train)
+X_test_vec = vectorizer.transform(X_test)
+
+# Train model
+model = LinearRegression()
+model.fit(X_train_vec, y_train)
+
+# Predict and evaluate
+y_pred = model.predict(X_test_vec)
+mse = mean_squared_error(y_test, y_pred)
+print(f"Mean Squared Error: {mse:.2f}")
+```
+
+### Classification Task (Binned Misery Levels)
+
+```python
+import numpy as np
+from sklearn.ensemble import RandomForestClassifier
+
+# Create misery level bins
+def bin_misery(score):
+    if score < 33:
+        return "Low"
+    elif score < 67:
+        return "Medium"
+    else:
+        return "High"
+
+df['misery_level'] = df['Score'].apply(bin_misery)
+
+# Classification pipeline
+X = df['Misery'].values
+y = df['misery_level'].values
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+X_train_vec = vectorizer.fit_transform(X_train)
+X_test_vec = vectorizer.transform(X_test)
+
+clf = RandomForestClassifier(n_estimators=100, random_state=42)
+clf.fit(X_train_vec, y_train)
+
+accuracy = clf.score(X_test_vec, y_test)
+print(f"Classification Accuracy: {accuracy:.2f}")
+```
+
+## Applications
+
+This dataset is valuable for:
+
+1. **Emotion Recognition**: Developing models to predict emotional responses to textual scenarios
+2. **Psychological Research**: Understanding factors that contribute to human distress
+3. **Content Moderation**: Identifying potentially distressing content
+4. **Mental Health Applications**: Building tools for emotional support and intervention
+5. **Game Design**: Creating balanced difficulty curves in narrative games
+6. **Educational Tools**: Teaching empathy and emotional intelligence
+
+## Ethical Considerations
+
+- **Content Warning**: Dataset contains descriptions of distressing scenarios including accidents, medical emergencies, and personal tragedies
+- **Subjectivity**: Misery scores reflect subjective human judgments and may vary across cultures and individuals
+- **Bias**: Original data sources may contain demographic or cultural biases
+- **Use Responsibly**: Should not be used to cause distress or for malicious purposes
+
+## Limitations
+
+1. **Cultural Bias**: Ratings may reflect Western cultural perspectives on distress
+2. **Temporal Bias**: Scenarios reflect contemporary (2010s-2020s) life situations
+3. **Subjectivity**: Individual misery perceptions may vary significantly
+4. **Limited Scope**: May not cover all possible distressing scenarios
+5. **Language**: English-only content limits cross-cultural applicability
+
+## Citation
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@dataset{misery_index_2024,
+  title={Misery Index Dataset: Textual Scenarios with Emotional Distress Ratings},
+  author={[Author Names]},
+  year={2024},
+  url={https://huggingface.co/datasets/path/to/misery-index},
+  note={Dataset of 516 scenarios with misery ratings from 0-100}
+}
+```
+
+## License
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+## Dataset Card Contact
+
+For questions or issues regarding this dataset, please [open an issue](https://huggingface.co/datasets/path/to/misery-index/discussions) or contact the dataset maintainers.