Emtract Distilbert Base Uncased Emotion
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Emtract Distilbert Base Uncased Emotion
Developed by vamossyd
A financial social media sentiment analysis model fine-tuned on DistilBERT, specialized in analyzing text sentiment from platforms like StockTwits
Downloads 797
Release Time : 5/17/2022
Model Overview
This model is specifically designed to analyze seven emotions in financial social media texts (e.g., StockTwits): neutral, happiness, sadness, anger, disgust, surprise, and fear
Model Features
Optimized for Financial Social Media
Specifically optimized with 10,000 financial social messages from the StockTwits platform
Multi-emotion Classification
Capable of identifying seven different emotions: neutral, happiness, sadness, anger, disgust, surprise, and fear
Efficient Architecture
Based on DistilBERT architecture, reducing computational resource requirements while maintaining performance
Model Capabilities
Text Sentiment Classification
Financial Social Media Analysis
Multi-label Emotion Recognition
Use Cases
Financial Analysis
Investor Sentiment Monitoring
Analyze sentiment changes of investors towards specific stocks on platforms like StockTwits
Can be used to predict market fluctuations
IPO Performance Prediction
Predict IPO performance through social media sentiment analysis
Related research has been published
Academic Research
Earnings Announcement Analysis
Study investor sentiment changes before and after earnings announcements
Related papers have been published
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