Open-source roberta_goEmotion text classification model - Accurately identify 28 different emotions

Roberta Goemotion

Developed by bsingh

A text classification model based on the RoBERTa architecture, specifically fine-tuned for the GoEmotions dataset to recognize 28 different emotions.

Text Classification

Transformers

EnglishOpen Source License:MIT #Multi-emotion classification #Reddit comment analysis #RoBERTa fine-tuning

Downloads 47

Release Time : 3/2/2022

Model Overview

This model is designed for sentiment analysis tasks, capable of identifying a variety of emotions expressed in text, including admiration, anger, joy, and 28 other emotion categories.

Model Features

Multi-emotion recognition

Capable of identifying 28 different emotion categories, including both positive and negative emotions.

Trained on Reddit data

Trained using real comment data from Reddit, making it suitable for social media text analysis.

RoBERTa architecture

Fine-tuned based on the powerful RoBERTa pre-trained model, offering strong text comprehension capabilities.

Model Capabilities

Text sentiment classification

Multi-label emotion recognition

Social media text analysis

Use Cases

Social media analysis

User comment sentiment analysis

Analyze the emotional tendencies in user comments on Reddit or other social media platforms.

Can identify 28 different emotions, helping to understand user sentiments.

Customer service

Customer feedback sentiment analysis

Analyze emotional tendencies in customer feedback.

Helps identify customer satisfaction and potential issues.

🚀 RoBERTa-based Text Classification Model for GoEmotions

This model is designed for text classification on the GoEmotions dataset. It aims to classify text into one of 28 emotions, providing a powerful tool for understanding emotional expressions in text data.

✨ Features

Trained on the GoEmotions dataset, which includes 58k labeled Reddit comments with 28 emotions, such as admiration, amusement, anger, etc.
The model can handle text data where each datapoint may have multiple emotion labels.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

Model Overview

This model is trained for the GoEmotions dataset which contains 58k labeled Reddit comments with 28 emotions: admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral.

Training Details

The training script is provided here: https://github.com/bsinghpratap/roberta_train_goEmotion
If you have trouble running the model, please feel free to start an issue in the repo, and the author will try to respond as soon as possible.
The model works well on most of the emotions except: 'desire', 'disgust', 'embarrassment', 'excitement', 'fear', 'grief', 'nervousness', 'pride', 'relief', 'remorse', 'surprise'.
The author will try to fine - tune the model further and update if RoBERTa achieves better performance.
Each text datapoint can have more than 1 label. Most of the training set had 1 label: Counter({1: 36308, 2: 6541, 3: 532, 4: 28, 5: 1}). Currently, the first label for each datapoint is used, which, although not ideal, does a decent job.

Model Performance

Emotion	GoEmotions Paper	RoBERTa	Support
admiration	0.65	0.62	504
amusement	0.80	0.78	252
anger	0.47	0.44	197
annoyance	0.34	0.22	286
approval	0.36	0.31	318
caring	0.39	0.24	114
confusion	0.37	0.29	139
curiosity	0.54	0.48	233
disappointment	0.28	0.18	127
disapproval	0.39	0.26	220
gratitude	0.86	0.84	288
joy	0.51	0.47	116
love	0.78	0.68	169
neutral	0.68	0.61	1606
optimism	0.51	0.52	120
realization	0.21	0.15	109
sadness	0.49	0.42	108

📄 License

This project is licensed under the MIT license.

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