Text Mining for Mental Disease Screening for Social Network Users

DOI

10.2991/978-2-38476-422-8_4How to use a DOI?

Keywords

Text mining; Social networks; Mental illness screening

Abstract

The widespread popularity of mobile internet has made the public more frequently share their lives and express their thoughts and emotions on social networks. In this context, Twitter has become the world’s largest user base social platform, accumulating a large amount of user generated text information. In order to gain a deeper understanding of the emotional tendencies in these textual information, we used the Sentient140 dataset for analysis. Through exploratory data analysis of Twitter text information, we compared different feature extraction methods and classification algorithms. This process not only helps us understand the emotional expression of Twitter users, but also provides a foundation for subsequent analysis. By evaluating the F1 value, we determined the optimal feature extraction and classification parameters for this task. The optimization of this step makes our model perform well in sentiment classification tasks, providing an effective method for text sentiment mining. The final analysis process and results of this study not only provide strong support for sentiment classification tasks based on text information, but also provide useful references for the diagnosis of mental diseases, such as depression. This means that our research is not only limited to social network analysis, but also has practical application potential in a wider range of health fields.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Chen Wang
PY  - 2025
DA  - 2025/06/12
TI  - Text Mining for Mental Disease Screening for Social Network Users
BT  - Proceedings of the 2025 4th International Conference on Humanities, Wisdom Education and Service Management (HWESM 2025)
PB  - Atlantis Press
SP  - 19
EP  - 27
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-422-8_4
DO  - 10.2991/978-2-38476-422-8_4
ID  - Wang2025
ER  -