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Hebemo Fear

Developed by avichr
HebEMO is a tool designed to detect sentiment polarity and extract emotions from modern Hebrew user-generated content, trained on a unique COVID-19 related dataset with superior performance.
Downloads 111
Release Time : 3/2/2022

Model Overview

HebEMO specializes in sentiment analysis of modern Hebrew, capable of identifying sentiment polarity (positive/neutral/negative) and eight basic emotions (anger, disgust, anticipation, fear, joy, sadness, surprise, and trust).

Model Features

High-performance Emotion Recognition
Achieves an outstanding weighted average F1 score of 0.96 in sentiment polarity classification tasks, surpassing similar English models.
Multi-emotion Detection
Capable of identifying eight basic emotions, with F1 scores as high as 0.96-0.97 for emotions like anger and disgust.
Large-scale Training Data
Trained on 150MB of Hebrew user-generated content, including 350,000 sentences and 7 million words.
Easy Integration
Provides online demos on Hugging Face and Colab, supports installation via pip.

Model Capabilities

Text Sentiment Polarity Analysis
Multi-emotion Recognition
Hebrew Natural Language Processing
User-generated Content Analysis

Use Cases

Social Media Analysis
News Comment Sentiment Analysis
Analyze sentiment tendencies in user comments on news websites
Accurately identifies negative, neutral, and positive comments
Market Research
Product Feedback Emotion Analysis
Analyze emotional responses of Hebrew-speaking users to products
Identifies specific emotions such as joy and anger
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