Identifying emotions from text is crucial for a variety of real world tasks.
We consider the two largest now-available corpora for emotion classification:
GoEmotions, with 58k messages labelled by readers, and Vent, with 33M
writer-labelled messages. We design a benchmark and evaluate several feature
spaces and learning algorithms, including two simple yet novel models on top of
BERT that outperform previous strong baselines on GoEmotions. Through an
experiment with human participants, we also analyze the differences between how
writers express emotions and how readers perceive them. Our results suggest
that emotions expressed by writers are harder to identify than emotions that
readers perceive. We share a public web interface for researchers to explore
our models.

本研究利用 GoEmotions 和 Vent 两个情感分类的大型数据集，通过多种特征空间和学习算法设计了一个基准测试，并在 BERT 基础上提出了两个简单却有效的模型，超过了先前的强基线。通过人体实验分析了作家表达情感和读者感知情感之间的差异，证明了作家表达的情感更难被识别。研究者可以通过我们提供的公共网络界面来研究我们的模型。