Hebemo Fear

🚀 HebEMO - Emotion Recognition Model for Modern Hebrew

HebEMO is a powerful tool designed to detect polarity and extract emotions from modern Hebrew User-Generated Content (UGC). It was trained on a unique Covid-19 related dataset that we collected and annotated, achieving remarkable results.

🚀 Quick Start

HebEMO offers high performance in both polarity classification and emotion detection. For polarity classification, it achieved a weighted average F1 - score of 0.96. In emotion detection, most emotions reached an F1 - score between 0.78 and 0.97, except for surprise which had an F1 - score of 0.41. These results outperform the best - reported performance, even when compared to English language models.

✨ Features

Emotion UGC Data Description

Our UGC data consists of comments on news articles from 3 major Israeli news sites, collected between January 2020 and August 2020. The data is about 150 MB in size, containing over 7 million words and 350K sentences. Approximately 2000 sentences were annotated by crowd members (3 - 10 annotators per sentence) for overall sentiment (polarity) and eight emotions: anger, disgust, anticipation, fear, joy, sadness, surprise, and trust. The percentage of sentences in which each emotion appeared is shown in the table below.

Performance

Emotion Recognition

The above metrics are for the positive class (meaning, the emotion is reflected in the text).

Sentiment (Polarity) Analysis

The sentiment (polarity) analysis model is also available on AWS! For more information, visit AWS' git

💻 Usage Examples

Basic Usage

Emotion Recognition Model

An online model can be found at huggingface spaces or as colab notebook

# !pip install pyplutchik==0.0.7
# !pip install transformers==4.14.1

!git clone https://github.com/avichaychriqui/HeBERT.git
from HeBERT.src.HebEMO import *
HebEMO_model = HebEMO()

HebEMO_model.hebemo(input_path = 'data/text_example.txt')
# return analyzed pandas.DataFrame  

hebEMO_df = HebEMO_model.hebemo(text='החיים יפים ומאושרים', plot=True)

For sentiment classification model (polarity ONLY):

from transformers import AutoTokenizer, AutoModel, pipeline

tokenizer = AutoTokenizer.from_pretrained("avichr/heBERT_sentiment_analysis") #same as 'avichr/heBERT' tokenizer
model = AutoModel.from_pretrained("avichr/heBERT_sentiment_analysis")

# how to use?
sentiment_analysis = pipeline(
    "sentiment-analysis",
    model="avichr/heBERT_sentiment_analysis",
    tokenizer="avichr/heBERT_sentiment_analysis",
    return_all_scores = True
)

sentiment_analysis('אני מתלבט מה לאכול לארוחת צהריים')	
>>>  [[{'label': 'neutral', 'score': 0.9978172183036804},
>>>  {'label': 'positive', 'score': 0.0014792329166084528},
>>>  {'label': 'negative', 'score': 0.0007035882445052266}]]

sentiment_analysis('קפה זה טעים')
>>>  [[{'label': 'neutral', 'score': 0.00047328314394690096},
>>>  {'label': 'positive', 'score': 0.9994067549705505},
>>>  {'label': 'negative', 'score': 0.00011996887042187154}]]

sentiment_analysis('אני לא אוהב את העולם')
>>>  [[{'label': 'neutral', 'score': 9.214012970915064e-05}, 
>>>  {'label': 'positive', 'score': 8.876807987689972e-05}, 
>>>  {'label': 'negative', 'score': 0.9998190999031067}]]

📄 License

If you used this model, please cite us as: Chriqui, A., & Yahav, I. (2022). HeBERT & HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition. INFORMS Journal on Data Science, forthcoming.

@article{chriqui2021hebert,
  title={HeBERT \& HebEMO: a Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition},
  author={Chriqui, Avihay and Yahav, Inbal},
  journal={INFORMS Journal on Data Science},
  year={2022}
}

Contact us

Avichay Chriqui
Inbal yahav
The Coller Semitic Languages AI Lab
Thank you, תודה, شكرا