The construction of intervention mode for people with depression based on human-computer interaction

Abstract

For most of people, the burden caused by COVID-19 is not only physical, but also spiritual. Several research studies have shown that the negative emotions caused by COVID-19 have increased significantly worldwide, but there are not effective emotional interventions for people in most countries. Therefore, this negative emotional trend is likely to expand further and cause more adverse effects, such as pathological depression. By applying python to crawl the posts of depressed patients on Chinese social media platforms, and using natural language processing to quantitatively analyze the words with high frequency, this paper summarizes the text characteristics and depression degrees of depressives' posts on Chinese social media platforms, so as to present a preliminary psychological intervention mode. Then, the paper proposes a more targeted psychological intervention mode for depressives with different characteristics based on the paradigm of human-computer interaction and the results of the questionnaire. The study finds that the patients with sufficient self-regulation abilities and the patients without self-regulation abilities do not meet the high-frequency word extraction results, while patients with insufficient self-regulation abilities do. Additionally, for these three types of patients, different psychological intervention modes can be established based on different interaction paradigms.