Depression Prediction Model based on NLP

Abstract. With the acceleration of modern society's pace, depression has become a common mental health issue. This study aims to develop a depression detection model based on natural language processing (NLP) technology to automatically identify potential depression patients. First, collect data from the Weibo platform using web scraping techniques, and then use NLP tools such as XLNet for in-depth analysis of the text data. In the model evaluation phase, the paper uses key metrics such as accuracy (ACC) to assess the effectiveness of NLP techniques in predicting depression. Experimental results indicate that the XLNet model performed the best among all tested models, achieving an accuracy of 81%, thus confirming the feasibility of using NLP technology for depression prediction. The significance of this study lies in providing a new method for mental health monitoring, which aids in the early detection and intervention of depression, with important social value and application prospects.