Applying Machine Learning to Detect Depression-Related Texts on Social Networks

Autor:	Kuralay Turganbay, Batyrkhan Omarov, Meruyert Yerekesheva, Zhazira Kozhamkulova, Lyailya Tukenova, Shirinkyz Shekerbekova
Rok vydání:	2021
Předmět:	Social network business.industry Computer science Kazakh Machine learning computer.software_genre language.human_language Connection (mathematics) ComputingMethodologies_PATTERNRECOGNITION Suicidal behavior language Artificial intelligence business computer Depression (differential diagnoses)
Zdroj:	Communications in Computer and Information Science ISBN: 9789811636592
DOI:	10.1007/978-981-16-3660-8_15
Popis:	This interdisciplinary study is aimed at determining the informative signs of behavior of users of the social network Vkontakte of the Kazakh segment in connection with the level of severity of signs of depression in them. We applied six machine learning algorithms with different features to depression related post detection problem. Our experimental results show that the problem can be successfully solved and applied to detect depressive or suicidal behavior or texts in online user contents. Experiment results with depressive and suicide related texts detection show that we can achieve high accuracy in depression related text classification using the collected dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4b626af504fabe8fc7feff70dca1d6fc https://doi.org/10.1007/978-981-16-3660-8_15 Zobrazit plný text záznamu