Depression Detection in Twitter Tweets Using Machine Learning Classifiers

Kumar, Shruthi K; Dinesh, Nanditha; Nitha, L.

doi:10.1109/icps55917.2022.00023

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A Eeg and Ems Based Depression Recognition1

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2023

2024

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Cited by 7 publications

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“…II. LITERATURE SURVEY 1) S. K. Kumar, N. Dinesh and N. L et al [1] proposed Using machine learning classifiers, detect depression in tweets on Twitter.…”

Section: A Eeg and Ems Based Depression Recognitionmentioning

confidence: 99%

Depression Detection Using EEG-Signals

Reddy¹,

Nikhitha²,

Reddy³

et al. 2023

IJRASET

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The most severe kind of mood disorder, depression, has a significant impact on mental health and causes difficulties in daily living. Electroencephalogram (EEG) signals can detect this mood condition. EEG signal analysis is a tedious, timeconsuming, and highly specialized method for manually diagnos- ing depression. Therefore, a fully automated depression detection system developed using EEG signals will be useful to clinicians. In this research work, a CNN based hybrid model was designed and this model resulted with 90percent accuracy, whereas with SVM it resulted in 60.

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“…II. LITERATURE SURVEY 1) S. K. Kumar, N. Dinesh and N. L et al [1] proposed Using machine learning classifiers, detect depression in tweets on Twitter.…”

Section: A Eeg and Ems Based Depression Recognitionmentioning

confidence: 99%