利用深度学习算法检测抑郁症

Alaa Zaghloul, Omar Khaled, Rania Elgohary
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引用次数: 0

摘要

:本研究旨在提供一个抑郁症检测项目,利用文本分析和自然语言处理(NLP)来识别抑郁症状。为了对推文大数据集进行情感分析,本项目将采用深度学习模型。社交媒体平台已发展成为个人表达思想和情感的场所。我们的目标是创建一个聊天平台,让用户能够与朋友、同事或陌生人进行互动,同时利用文本分析来识别悲伤情绪。有几种浏览器可以用来访问网站和指导如何与网站互动。我们的研究将强调早期抑郁症检测的意义及其对社区福祉可能产生的影响,包括对当地公司生产力和医疗成本的不利影响。本项目旨在提高公众对早期识别优势的认识,并提供一种基于深度学习的方法,帮助人们识别抑郁症并获得必要的帮助。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Depression Detection using Deep Learning Algorithms
: This research study aims to provide a depression detection project that uses text analysis and natural language processing (NLP) to identify symptoms of depression. In order to conduct sentiment analysis on big datasets of tweets, this project will employ a deep learning model. Social media platforms have evolved into places where individuals express their ideas and feelings. Our objective is to create a chat platform that enables users to interact with friends, coworkers, or complete strangers while using text analysis to identify sadness. There are several browsers that can be used to visit the website and guidance on interacting with it. The significance of early depression detection and its possible effects on community well-being—including detrimental effects on local company productivity and healthcare costs—will be emphasized in our research. The purpose of this project is to increase public awareness of the advantages of early identification and to offer a deep learning-based approach to assist people in identifying depression and obtaining the necessary assistance.
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