HeBERT and HebEMO: A Hebrew BERT Model and a Tool for Polarity Analysis and Emotion Recognition

Sentiment analysis of user-generated content (UGC) can provide valuable information across numerous domains, including marketing, psychology, and public health. Currently, there are very few Hebrew models for natural language processing in general, and for sentiment analysis in particular; indeed, it is not straightforward to develop such models because Hebrew is a morphologically rich language (MRL) with challenging characteristics. Moreover, the only available Hebrew sentiment analysis model, based on a recurrent neural network, was developed for polarity analysis (classifying text as positive, negative, or neutral) and was not used for detection of finer-grained emotions (e.g., anger, fear, or joy). To address these gaps, this paper introduces HeBERT and HebEMO. HeBERT is a transformer-based model for modern Hebrew text, which relies on a BERT (bidirectional encoder representations from transformers) architecture. BERT has been shown to outperform alternative architectures in sentiment analysis and is suggested to be particularly appropriate for MRLs. Analyzing multiple BERT specifications, we find that whereas model complexity correlates with high performance on language tasks that aim to understand terms in a sentence, a more parsimonious model better captures the sentiment of an entire sentence. Notably, regardless of the complexity of the BERT specification, our BERT-based language model outperforms all existing Hebrew alternatives on all language tasks examined. HebEMO is a tool that uses HeBERT to detect polarity and extract emotions from Hebrew UGC. HebEMO is trained on a unique COVID-19-related UGC data set that we collected and annotated for this study. Data collection and annotation followed an active learning procedure that aimed to maximize predictability. We show that HebEMO yields a better performance accuracy for polarity classification. Emotion detection reaches high performance for various target emotions, with the exception of surprise, which the model failed to capture. These results are better than the best reported performance, even among English-language models of emotion detection.