Detecting depression using K-Nearest Neighbors (KNN) classification technique

M. R. Islam; Abu Raihan M Kamal; Naznin Sultana; Robiul Islam; Mohammad Ali Moni; A. Ulhaq

doi:10.1109/IC4ME2.2018.8465641

Detecting depression using K-Nearest Neighbors (KNN) classification technique

M. R. Islam, Abu Raihan M Kamal, Naznin Sultana, Robiul Islam, Mohammad Ali Moni, A. Ulhaq

Research output: Book chapter/Published conference paper › Conference paper › peer-review

43 Citations (Scopus)

Abstract

Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. Also, it has been an upcoming research field and has picked an established position globally. In this paper, we considered depression problems among various Facebook users. Already, a number of researchers have studied and applied many techniques to detect depression, but still need to detect accurately from social network data. So, we investigate the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions. We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.

Original language	English
Title of host publication	2018 International Conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018
Publisher	IEEE Xplore
Number of pages	4
ISBN (Electronic)	9781538647752
ISBN (Print)	9781538647769
DOIs	https://doi.org/10.1109/IC4ME2.2018.8465641
Publication status	Published - 20 Sept 2018
Event	2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering : IC4ME2 - University of Rajshahi, Rajshahi, Bangladesh Duration: 08 Feb 2018 → 09 Feb 2018 https://www.aconf.org/conf_147728.html http://dept.ru.ac.bd/ic4me2/2018/

Conference

Conference	2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering
Country/Territory	Bangladesh
City	Rajshahi
Period	08/02/18 → 09/02/18
Internet address	https://www.aconf.org/conf_147728.html http://dept.ru.ac.bd/ic4me2/2018/

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Cite this

@inproceedings{a92f2c050ec04715a492ac4826570de1,

title = "Detecting depression using K-Nearest Neighbors (KNN) classification technique",

abstract = "Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. Also, it has been an upcoming research field and has picked an established position globally. In this paper, we considered depression problems among various Facebook users. Already, a number of researchers have studied and applied many techniques to detect depression, but still need to detect accurately from social network data. So, we investigate the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions. We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.",

keywords = "Emotions, Depression, Sentiment analysis, Predictions, Social media",

author = "Islam, {M. R.} and Kamal, {Abu Raihan M} and Naznin Sultana and Robiul Islam and Moni, {Mohammad Ali} and A. Ulhaq",

note = "Includes bibliographical references.; 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering : IC4ME2 ; Conference date: 08-02-2018 Through 09-02-2018",

year = "2018",

month = sep,

day = "20",

doi = "10.1109/IC4ME2.2018.8465641",

language = "English",

isbn = "9781538647769",

booktitle = "2018 International Conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018",

publisher = "IEEE Xplore",

url = "https://www.aconf.org/conf_147728.html, http://dept.ru.ac.bd/ic4me2/2018/",

}

Islam, MR, Kamal, ARM, Sultana, N, Islam, R, Moni, MA & Ulhaq, A 2018, Detecting depression using K-Nearest Neighbors (KNN) classification technique. in 2018 International Conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018., 8465641, IEEE Xplore, 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering , Rajshahi, Bangladesh, 08/02/18. https://doi.org/10.1109/IC4ME2.2018.8465641

Detecting depression using K-Nearest Neighbors (KNN) classification technique. / Islam, M. R.; Kamal, Abu Raihan M; Sultana, Naznin et al.
2018 International Conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018. IEEE Xplore, 2018. 8465641.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Detecting depression using K-Nearest Neighbors (KNN) classification technique

AU - Islam, M. R.

AU - Kamal, Abu Raihan M

AU - Sultana, Naznin

AU - Islam, Robiul

AU - Moni, Mohammad Ali

AU - Ulhaq, A.

N1 - Includes bibliographical references.

PY - 2018/9/20

Y1 - 2018/9/20

N2 - Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. Also, it has been an upcoming research field and has picked an established position globally. In this paper, we considered depression problems among various Facebook users. Already, a number of researchers have studied and applied many techniques to detect depression, but still need to detect accurately from social network data. So, we investigate the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions. We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.

AB - Social networks have developed as a promising point for everybody to communicate with their interested friend and share their opinions, photos, and videos. Also, it has been an upcoming research field and has picked an established position globally. In this paper, we considered depression problems among various Facebook users. Already, a number of researchers have studied and applied many techniques to detect depression, but still need to detect accurately from social network data. So, we investigate the possibility to utilize Facebook data and apply KNN (k-nearest neighbors) classification technique for detecting depressive emotions. We do believe that our investigation and approach might be helpful to raise consciousness in online social network users.

KW - Emotions

KW - Depression

KW - Sentiment analysis

KW - Predictions

KW - Social media

U2 - 10.1109/IC4ME2.2018.8465641

DO - 10.1109/IC4ME2.2018.8465641

M3 - Conference paper

SN - 9781538647769

BT - 2018 International Conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018

PB - IEEE Xplore

T2 - 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering

Y2 - 8 February 2018 through 9 February 2018

ER -

Detecting depression using K-Nearest Neighbors (KNN) classification technique

Abstract

Conference

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