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Bert 43 Multilabel Emotion Detection

Developed by borisn70
A multilabel sentiment classification model fine-tuned based on bert-base-uncased, capable of classifying English texts into 43 emotion categories
Downloads 326
Release Time : 4/1/2024

Model Overview

This model can perform multilabel sentiment classification on English texts, identifying 43 different emotion categories, suitable for scenarios such as social media public opinion monitoring and customer feedback analysis.

Model Features

Multilabel Classification
Capable of identifying multiple emotions in text simultaneously, rather than a single emotion
Broad Emotion Coverage
Supports classification into 43 different emotion categories
High Performance
Achieves 92.02% accuracy on the validation set
Comprehensive Training Data
Trained on multiple high-quality sentiment datasets

Model Capabilities

Sentiment Analysis
Emotion Recognition
Feeling Classification
Label Classification

Use Cases

Social Media Analysis
Public Opinion Monitoring
Analyze users' emotional reactions to specific topics on social media
Can identify multiple emotional tendencies to help understand public sentiment
Customer Service
Feedback Analysis
Analyze emotional tendencies in customer feedback
Helps identify customer satisfaction and potential problem areas
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