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.

本研究利用预训练转换器对多语言和多领域的输入文本进行情感分析，使用基于趋势-唤醒维度的情感分析方法，比传统方法更细致地区分不同的情感，并发现模型大小对预测质量有显著影响，可自信地预测不同语言的趋势性和唤醒性。

使用多语言预训练变压器量化文本情感的价值和唤起程度