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Top1.1 Machine Learning
The typical procedure of ML in identifying depression embodies multiple stages, with the primary ones elaborated in Figure 1. In the data collection phase, a large amount of social media textual data, including user-generated content and comments, needs to be gathered. The preprocessing stage involves operations such as cleaning, deduplication, and tokenization to eliminate irrelevant information and noise, making the data more standardized and easy to process. Feature extraction utilizes various algorithms and techniques to extract depression-related features from the textual data, such as word frequency and sentiment tendencies. Finally, by training the model to classify and recognize these features, depression detection is achieved (Figure 1).
Figure 1. Data Preprocessing and Model Training Flowchart
In the wake of the advancements in deep learning, an expanding repertoire of algorithms has been extensively integrated for the purpose of detecting depression. Deep learning technology can automatically extract deep features from text, understand the meanings of words and sentences in context, and better identify depression-related text. Compared to traditional methodologies, a plethora of emerging novel models and algorithms in recent years have demonstrated enhanced accuracy in detecting depression and proficiency in addressing time-series issues. These advancements play a crucial role in the prediction and diagnosis of depression, particularly considering the significance of early and sequential information for patients afflicted with depressive disorders. Therefore, deep learning considers early-time series, preventing the algorithm from inaccurately identifying depression patients, and exhibits better generalization performance.