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。

揭示基于文本的情绪检测的限制

Uncovering the Limits of Text-based Emotion Detection

Understanding emotion expressed in language has a wide range of applications,
from building empathetic chatbots to detecting harmful online behavior.
Advancement in this area can be improved using large-scale datasets with a
fine-grained typology, adaptable to multiple downstream tasks. We introduce
GoEmotions, the largest manually annotated dataset of 58k English Reddit
comments, labeled for 27 emotion categories or Neutral. We demonstrate the high
quality of the annotations via Principal Preserved Component Analysis. We
conduct transfer learning experiments with existing emotion benchmarks to show
that our dataset generalizes well to other domains and different emotion
taxonomies. Our BERT-based model achieves an average F1-score of .46 across our
proposed taxonomy, leaving much room for improvement.

介绍了 GoEmotions，这是目前最大的手动标注的数据集之一，其中包含 58k 英文 Reddit 评论，并将其标记为 27 种情感类别或中性。通过传递学习实验，证明了该数据集具有良好的泛化性能，BERT-based 情感检测模型取得了.46 的平均 F1 分数。