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- W4315777718 abstract "Aiming at the problem that depression has become severe social problems and most of patients can not be diagnosed early. As social media has become popular in the world like Chinese microblogs and twitters, a large amount of data can be used for the researches, especially the contents published originally can be used to detect whether publisher is depressed. However, depression prediction is not the same as text classification, it is difficult to find a unified standard to determine depression. In this regard, a method combining machine learning and TF-IDF is proposed. The method treats micro-blog contents as a whole to generate features to predict whether the micro-blog users get depressed. The experiment shows that the proposed method can effectively identify potential depressive patients among micro-blog users." @default.
- W4315777718 created "2023-01-13" @default.
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- W4315777718 date "2022-12-16" @default.
- W4315777718 modified "2023-10-16" @default.
- W4315777718 title "Combing TF-IDF with machine learning to detect depression in Microblog" @default.
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- W4315777718 doi "https://doi.org/10.1109/iciea54703.2022.10006146" @default.
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