With the development of the Internet, more and more people prefer to confide their sentiments in the virtual world, especially those with depression. The social media where people with depression collectively leave messages is called the "Tree Hole". The purpose of this article is to support the "Tree Hole" rescue volunteers to help patients with depression, especially after the outbreak of COVID-19 and other major events, to guide the crisis intervention of patients with depression. Based on the message data of "Tree Hole" named "Zou Fan", this paper used a deep learning model and sentiment scoring algorithm to analyze the fluctuation characteristics sentiment of user's message in different time dimensions. Through detailed investigation of the research results, we found that the number of "Tree Hole" messages in multiple time dimensions is positively correlated to emotion. The longer the "Tree Hole" is formed, the more negative the emotion is, and the outbreak of COVID-19 and other major events have obvious effects on the emotion of the messages. In order to improve the efficiency of "Tree Hole" rescue, volunteers should focus on the long-formed "Tree Hole" and the user groups that are active in the early morning. This research is of great significance for the emotional guidance of online mental health patients, especially the crisis intervention for depression patients after the outbreak of COVID-19 and other major events.
Keywords: Adversarial training; BERT+BiLSTM; Depression; Sentiment analysis; Time feature.
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022.