TY  - JOUR
Y1  - 2019///
N1  - cited By 2
A1  - Balakrishnan, V.
A1  - Govindan, V.
A1  - Arshad, N.I.
A1  - Shuib, L.
A1  - Cachia, E.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100495499&doi=10.22452%2fmjcs.sp2019no3.6&partnerID=40&md5=944dca5c23e0ca929fd5e6bec1a3cdea
JF  - Malaysian Journal of Computer Science
VL  - 2019
IS  - Specia
TI  - Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
SN  - 01279084
N2  - With the advent of Web 2.0 technologies such as social media, online text sources provide large scale data repositories out of which valuable knowledge about human emotions can be derived. This paper aims to (i) detect and classify emotions of the Facebook diabetes community, (ii) examine the relationship of emotion and Facebook reactions, and (iii) identify user reaction predictors for each of the emotion. A total of 15K posts were randomly selected from several official Facebook diabetes support groups. Pre-processing was administered, resulting in 2475 Facebook posts for further analysis in this study. Emotion detection was first administered using Indico API, with results revealing anger, sadness and fear to be the top most emotions experienced, whilst love and wow emerged as the highest-ranking reactions. Precision and recall indicate the performance of the emotion detection mechanism ranged between 65 â?? 82 for all the emotions, compared to the human annotation. The average F-score recorded was 78. Both love and wow were found to significantly predict joy and fear, whereas angry was found to predict anger. The findings indicate that human emotions can be effectively detected based on usersâ?? textual communication, and significant relationships exists between several reactions and emotions © 2021,Malaysian Journal of Computer Science. All rights reserved
EP  - 97
ID  - scholars11858
AV  - none
PB  - Faculty of Computer Science and Information Technology
SP  - 87
ER  -