Datasets:
language:
- en
pretty_name: Enhanced Emotion Classification Dataset
version: 2
tags:
- text-classification
- emotion
- sentiment-analysis
- ekman-emotions
- text
license: mit
task_categories:
- text-classification
task_ids:
- sentiment-classification
Enhanced Emotion Classification Dataset (v2.0)
Dataset Description
This dataset is an enhanced version of the emotion classification dataset, including multiple sources of emotion data with Ekman emotion mapping. It contains a total of 240,426 samples across 7 emotion categories, with each sample labeled with its original data source.
Dataset Structure
The dataset is split into three parts:
- Train: 186,619 samples (77.6%)
- Validation: 31,086 samples (12.9%)
- Test: 22,721 samples (9.4%)
Emotion Categories
The dataset includes 7 Ekman basic emotions:
- neutral
- joy
- sadness
- anger
- fear
- surprise
- disgust
Data Sources
The dataset combines data from 7 different sources:
Movies_Reviews
- Movie review emotion data
- Contains 7 emotion categories
DailyDialog
- Real dialog data with multi-turn conversations
- Contains 7 emotion categories
GoEmotions
- Reddit comment data with colloquial expressions
- Contains 7 emotion categories
ISEAR
- International emotion research data with high-quality text
- Contains 7 emotion categories
MELD
- Multimodal emotion dialog data from the TV show "Friends"
- Contains 7 emotion categories
mteb_emotion
- Emotion analysis dataset with various emotion expressions
- Contains 7 emotion categories
Tweet Emotions
- Twitter tweet data including @mentions
- Contains 7 emotion categories
Data Format
Each CSV file contains the following columns:
text: Text contentlabel_text: Emotion label (one of the 7 emotion categories)source: Data source identifier (e.g., dailydialog, goemotions, tweetemotions, etc.)
File Structure
dataset_huggingface_enhance/
├── train.csv # Merged training set (186,619 samples)
├── val.csv # Merged validation set (31,086 samples)
├── test.csv # Merged test set (22,721 samples)
├── README.md # Dataset documentation
├── dataset_infos.json # Hugging Face dataset configuration
├── info.md # Detailed dataset statistics
├── label_list.txt # List of emotion labels
└── stats.py # Dataset statistics script
Usage
To use this dataset with Hugging Face Datasets library:
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("jiangchengchengNLP/Enhanced_Emotion_Classification_Dataset")
# Access specific splits
train_dataset = dataset["train"]
val_dataset = dataset["validation"]
test_dataset = dataset["test"]
Emotion Distribution
Training Set (186,619 samples)
- neutral: 51.11%
- joy: 21.64%
- sadness: 7.98%
- anger: 5.97%
- fear: 5.90%
- surprise: 5.11%
- disgust: 2.29%
Validation Set (31,086 samples)
- neutral: 40.96%
- joy: 24.15%
- sadness: 9.38%
- fear: 8.06%
- anger: 7.49%
- surprise: 7.06%
- disgust: 2.89%
Test Set (22,721 samples)
- neutral: 45.11%
- joy: 22.88%
- sadness: 9.13%
- anger: 7.61%
- fear: 6.67%
- surprise: 5.93%
- disgust: 2.67%
Notes
- The dataset has class imbalance, with neutral being the most common category (
40-51%) and disgust being the least common (2-3%). - Each sample includes a
sourcefield indicating its original data source, which allows for source-specific analysis. - Different data sources have different text styles, which may affect model performance.
- The dataset uses Ekman emotion mapping, which maps various emotion labels to the 7 basic emotions.
License
The dataset is released under the MIT License.
Citation
If you use this dataset in your research, please cite the original datasets:
- DailyDialog: https://aclanthology.org/I17-1099/
- GoEmotions: https://arxiv.org/abs/2005.00547
- Tweet Emotions: https://www.aclweb.org/anthology/W18-6212/
- MELD: https://arxiv.org/abs/1810.02508
- ISEAR: https://link.springer.com/article/10.1007/BF02112196
Version History
v2.0 (2026-01-07): Updated dataset with merged sources and source field
- Total samples: 240,426
- Added
sourcefield to all samples - Updated emotion distribution statistics
- Improved data quality and consistency
- Filtered all NaN values from the dataset
v1.0: Initial release