Quantifying Valence and Arousal in Text with Multilingual Pre-trained Transformers
This addresses the need for nuanced emotion analysis in text for applications like sentiment analysis, though it is incremental as it applies existing methods to a less-studied dimensional approach.
This work tackles the problem of predicting valence and arousal dimensions in text across multiple languages and domains using pre-trained Transformers, finding that model size significantly impacts prediction quality and fine-tuning large models enables confident multilingual predictions.
The analysis of emotions expressed in text has numerous applications. In contrast to categorical analysis, focused on classifying emotions according to a pre-defined set of common classes, dimensional approaches can offer a more nuanced way to distinguish between different emotions. Still, dimensional methods have been less studied in the literature. Considering a valence-arousal dimensional space, this work assesses the use of pre-trained Transformers to predict these two dimensions on a continuous scale, with input texts from multiple languages and domains. We specifically combined multiple annotated datasets from previous studies, corresponding to either emotional lexica or short text documents, and evaluated models of multiple sizes and trained under different settings. Our results show that model size can have a significant impact on the quality of predictions, and that by fine-tuning a large model we can confidently predict valence and arousal in multiple languages. We make available the code, models, and supporting data.