Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

Aritra Bandyopadhyay; K. Manjula Shenoy

doi:10.1007/978-3-030-73100-7_76

Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

Aritra Bandyopadhyay^*
, K. Manjula Shenoy

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
Editors	Kohei Arai
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	1097-1106
Number of pages	10
ISBN (Print)	9783030730994
DOIs	https://doi.org/10.1007/978-3-030-73100-7_76
Publication status	Published - 2021
Event	Future of Information and Communication Conference, FICC 2021 - Virtual, Online Duration: 29-04-2021 → 30-04-2021

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1363 AISC
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Future of Information and Communication Conference, FICC 2021
City	Virtual, Online
Period	29-04-21 → 30-04-21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-73100-7_76

Cite this

Bandyopadhyay, A., & Shenoy, K. M. (2021). Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter. In K. Arai (Ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC (pp. 1097-1106). (Advances in Intelligent Systems and Computing; Vol. 1363 AISC). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73100-7_76

Bandyopadhyay, Aritra ; Shenoy, K. Manjula. / Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter. Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. editor / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. pp. 1097-1106 (Advances in Intelligent Systems and Computing).

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title = "Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter",

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.",

author = "Aritra Bandyopadhyay and Shenoy, \{K. Manjula\}",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Future of Information and Communication Conference, FICC 2021 ; Conference date: 29-04-2021 Through 30-04-2021",

year = "2021",

doi = "10.1007/978-3-030-73100-7\_76",

language = "English",

isbn = "9783030730994",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Kohei Arai",

booktitle = "Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC",

address = "Germany",

}

Bandyopadhyay, A & Shenoy, KM 2021, Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter. in K Arai (ed.), Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Advances in Intelligent Systems and Computing, vol. 1363 AISC, Springer Science and Business Media Deutschland GmbH, pp. 1097-1106, Future of Information and Communication Conference, FICC 2021, Virtual, Online, 29-04-21. https://doi.org/10.1007/978-3-030-73100-7_76

Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter. / Bandyopadhyay, Aritra; Shenoy, K. Manjula.
Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. ed. / Kohei Arai. Springer Science and Business Media Deutschland GmbH, 2021. p. 1097-1106 (Advances in Intelligent Systems and Computing; Vol. 1363 AISC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

AU - Bandyopadhyay, Aritra

AU - Shenoy, K. Manjula

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85105946322

UR - https://www.scopus.com/pages/publications/85105946322#tab=citedBy

U2 - 10.1007/978-3-030-73100-7_76

DO - 10.1007/978-3-030-73100-7_76

M3 - Conference contribution

AN - SCOPUS:85105946322

SN - 9783030730994

T3 - Advances in Intelligent Systems and Computing

SP - 1097

EP - 1106

BT - Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC

A2 - Arai, Kohei

PB - Springer Science and Business Media Deutschland GmbH

T2 - Future of Information and Communication Conference, FICC 2021

Y2 - 29 April 2021 through 30 April 2021

ER -

Bandyopadhyay A, Shenoy KM. Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter. In Arai K, editor, Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC. Springer Science and Business Media Deutschland GmbH. 2021. p. 1097-1106. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-73100-7_76

Analyzing Performance of Classification Algorithms in Detection of Depression from Twitter

Abstract

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UN SDGs

All Science Journal Classification (ASJC) codes

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