Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

Aritra Bandyopadhyay, K. Manjula Shenoy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Depression is a mental disorder that is characterized by a general mood or feeling of low self-esteem, loss of interest towards daily activities and low energy within a particular person. It is a very serious mental condition and its automatic detection through online social media platforms like Twitter could help identifying depressed individuals remotely. This paper suggests a novel method to extract tweets indicating depression using word lists. Various classification algorithms like SVM, KNN, Naive Bayes and Random Forests have been used to classify the individual tweets as to whether they indicate depression in the subject or not. Metrics like F1-Score has been used the verify and compare the results of the models using an unseen test dataset.

Original languageEnglish
Title of host publicationAdvances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
EditorsKohei Arai
PublisherSpringer Science and Business Media Deutschland GmbH
Pages1097-1106
Number of pages10
ISBN (Print)9783030730994
DOIs
Publication statusPublished - 2021
EventFuture of Information and Communication Conference, FICC 2021 - Virtual, Online
Duration: 29-04-202130-04-2021

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1363 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

ConferenceFuture of Information and Communication Conference, FICC 2021
CityVirtual, Online
Period29-04-2130-04-21

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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