Depression Detection using Deep Learning Algorithms | ||||
| International Integrated Intelligent Systems | ||||
| Volume 1, Issue 1, February 2024, Page 1-7 PDF (502.68 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iiis.2024.342001 | ||||
 View on SCiNiTO | ||||
| Abstract | ||||
| 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 | ||||
| Keywords | ||||
| Natural Language Processing; Sentiment Analysis; Deep learning; Depression detection | ||||
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