Emotion corpora are typically sampled based on keyword/hashtag search or by
asking study participants to generate textual instances. In any case, these
corpora are not uniform samples representing the entirety of a domain. We
hypothesize that this practice of data acquisition leads to unrealistic
correlations between overrepresented topics in these corpora that harm the
generalizability of models. Such topic bias could lead to wrong predictions for
instances like "I organized the service for my aunt's funeral." when funeral
events are over-represented for instances labeled with sadness, despite the
emotion of pride being more appropriate here. In this paper, we study this
topic bias both from the data and the modeling perspective. We first label a
set of emotion corpora automatically via topic modeling and show that emotions
in fact correlate with specific topics. Further, we see that emotion
classifiers are confounded by such topics. Finally, we show that the
established debiasing method of adversarial correction via gradient reversal
mitigates the issue. Our work points out issues with existing emotion corpora
and that more representative resources are required for fair evaluation of
models predicting affective concepts from text.

我们研究了情绪语料库中存在的主题偏差以及相关的建模方法，并发现通过对抗性修正可以缓解此问题。我们的研究指出现有情绪语料库存在问题，并且需要更具代表性的资源来进行情感概念从文本中预测的公平评估。