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Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community

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 relati...

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Main Authors: Balakrishnan, V., Govindan, V., Arshad, N.I., Shuib, L., Cachia, E.
Format: Article
Published: Faculty of Computer Science and Information Technology 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100495499&doi=10.22452%2fmjcs.sp2019no3.6&partnerID=40&md5=944dca5c23e0ca929fd5e6bec1a3cdea
http://eprints.utp.edu.my/30177/
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spelling my.utp.eprints.301772022-03-25T06:36:20Z Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community Balakrishnan, V. Govindan, V. Arshad, N.I. Shuib, L. Cachia, E. 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 � 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 users� textual communication, and significant relationships exists between several reactions and emotions © 2021,Malaysian Journal of Computer Science. All rights reserved Faculty of Computer Science and Information Technology 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100495499&doi=10.22452%2fmjcs.sp2019no3.6&partnerID=40&md5=944dca5c23e0ca929fd5e6bec1a3cdea Balakrishnan, V. and Govindan, V. and Arshad, N.I. and Shuib, L. and Cachia, E. (2019) Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community. Malaysian Journal of Computer Science, 2019 (Specia). pp. 87-97. http://eprints.utp.edu.my/30177/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description 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 � 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 users� textual communication, and significant relationships exists between several reactions and emotions © 2021,Malaysian Journal of Computer Science. All rights reserved
format Article
author Balakrishnan, V.
Govindan, V.
Arshad, N.I.
Shuib, L.
Cachia, E.
spellingShingle Balakrishnan, V.
Govindan, V.
Arshad, N.I.
Shuib, L.
Cachia, E.
Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
author_facet Balakrishnan, V.
Govindan, V.
Arshad, N.I.
Shuib, L.
Cachia, E.
author_sort Balakrishnan, V.
title Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
title_short Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
title_full Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
title_fullStr Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
title_full_unstemmed Facebook User Reactions And Emotion: An Analysis Of Their Relationships Among The Online Diabetes Community
title_sort facebook user reactions and emotion: an analysis of their relationships among the online diabetes community
publisher Faculty of Computer Science and Information Technology
publishDate 2019
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100495499&doi=10.22452%2fmjcs.sp2019no3.6&partnerID=40&md5=944dca5c23e0ca929fd5e6bec1a3cdea
http://eprints.utp.edu.my/30177/
_version_ 1738657070897430528
score 13.211869

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