About: Cost-sensitive Boosting Pruning Trees for depression detection on Twitter

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #93004 Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
sameAs	Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
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dc:hasVersion	Cost-sensitive Boosting Pruning Trees for depression detection on Twitter (PDF)
Title	Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2022-01-25
Creator	Jialin Lyu Yinhai Wang Lei Tong Ling Li Long Chen Qianni Zhang Xiangrong Zhang Zheheng Jiang Feixiang Zhou Abdul Sadka Zhihua Liu Huiyu Zhou
status	peer reviewed published
Publisher	IEEE
abstract	Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. In this paper, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, we propose a novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. To comprehensively evaluate the classification capability of CBPT, we use additional three datasets from the UCI machine learning repository and CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression
Is Part Of	IEEE Transactions on Affective Computing https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/93004#authors
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #93004 Cost-sensitive Boosting Pruning Trees for depression detection on Twitter

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