Use of Ensemble Machine Learning to Detect Depression in Social Media Posts

Depression is a common and severe medical condition which adversely affects your feeling, thinking and acting. Depression can lead to suicide. It can trigger a slew of physical and emotional issues, as well as a reduction in your ability to function at work and home. Globally, depression affects mor...

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Bibliographic Details
Published in2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC) pp. 1396 - 1400
Main Authors Jagtap, Nakshatra, Shukla, Hrushikesh, Shinde, Vaibhavi, Desai, Sharmishta, Kulkarni, Vrushali
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.08.2021
Subjects
Communication systems
Depression
Depression detection
Ensemble learning
Machine learning
Machine learning algorithms
N-gram technique
Natural language processing
Social networking (online)
Support vector machines
Text preprocessing
TF-IDF
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Summary:Depression is a common and severe medical condition which adversely affects your feeling, thinking and acting. Depression can lead to suicide. It can trigger a slew of physical and emotional issues, as well as a reduction in your ability to function at work and home. Globally, depression affects more than 264 million individuals of all ages [1]. Since the younger generation is more reliant on social media, analyzing posts on social media will benefit in detecting depression. This research work has proposed a system to detect depression using ensembled learning and Natural Language Processing (NLP) techniques. Also, the proposed research work has compared the performance of multiple machine learning algorithms and the best performing configuration gave us the accuracy of 96.35%.
DOI:10.1109/ICESC51422.2021.9532838