  It is often difficult to correctly infer a writer's emotion from text
exchanged online, and differences in recognition between writers and readers
can be problematic. In this paper, we propose a new framework for detecting
sentences that create differences in emotion recognition between the writer and
the reader and for detecting the kinds of expressions that cause such
differences. The proposed framework consists of a bidirectional encoder
representations from transformers (BERT)-based detector that detects sentences
causing differences in emotion recognition and an analysis that acquires
expressions that characteristically appear in such sentences. The detector,
based on a Japanese SNS-document dataset with emotion labels annotated by both
the writer and three readers of the social networking service (SNS) documents,
detected "hidden-anger sentences" with AUC = 0.772; these sentences gave rise
to differences in the recognition of anger. Because SNS documents contain many
sentences whose meaning is extremely difficult to interpret, by analyzing the
sentences detected by this detector, we obtained several expressions that
appear characteristically in hidden-anger sentences. The detected sentences and
expressions do not convey anger explicitly, and it is difficult to infer the
writer's anger, but if the implicit anger is pointed out, it becomes possible
to guess why the writer is angry. Put into practical use, this framework would
likely have the ability to mitigate problems based on misunderstandings.
