Liking, sharing, commenting and reacting on Facebook: User behaviors’ impact on sentiment intensity
Document Type
Article
Publication Date
1-1-2019
Abstract
The form of communication on Facebook is not only limited to posting and commenting, but also includes sharing, liking and reacting. This study looks into how a Facebook diabetes community uses like, comment, share and reaction in expressing themselves online and how these distinctions can be used to improve sentiment classification from text extracted from the said group. An intensity formula using those behaviors was proposed and experimentations conducted using Weka. The findings reveal a model encompassing user behaviors is able to determine sentiment more accurately compared to one without, with a 94.6 percentage of accuracy. Additional analyses reveal behaviors such as liking, commenting and sharing to contribute more to the sentiment classification compared to reacting. This further cement the need to include such behavioral aspects into sentiment polarity calculation, as it would help algorithms achieve better predictability when classifying sentiment. © 2018 Elsevier Ltd
Keywords
Facebook, Like, Comment, Share, Reaction, Sentiment intensity
Divisions
fac_med,fsktm
Funders
University of Malaya, under research grant reference number: UMRG RP059C 17SBS
Publication Title
Telematics and Informatics
Volume
39
Publisher
Elsevier