Machine Learning Approach for Depression Detection in Japanese

Yutaka Miyaji Yuka Niimi
2021 Pacific Asia Conference on Language, Information and Computation  
In this paper, we describe the results of our research aimed at detecting depression in Japanese sentences. In the United States and elsewhere, machine learning approaches to detect depression from language have been demonstrated. However, in Japanese text, there are only two studies that have addressed the detection of depression. In this study, to detect depression based on linguistic features, even in documents that do not explicitly mention the topic of depression, we build a machine
more » ... g model that detects depression in Japanese by eliminating topics that suggest depression or depression. We also examine the accuracy when the parts of speech are limited and when the number of labels is aligned. In the performances of our models with 5-fold cross-validation, we were able to obtain a high evaluation.
dblp:conf/paclic/Niimi21 fatcat:c2vk45zd3zarhpghh554q4pxp4