@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 = "depression, Emotions, predictions, sentiment analysis, 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 = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018 ; 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",
series = "International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018",
publisher = "IEEE Xplore",
booktitle = "International Conference on Computer, Communication, Chemical, Material and Electronic Engineering, IC4ME2 2018",
}