Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70922

Identifying Sentiment Influences Provoked by Context Factors – Results from a Data Analytics Procedure Performed on Tweets

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dc.contributor.author Konadl, Daniel
dc.contributor.author Wörner, Janik
dc.contributor.author Leist, Susanne
dc.date.accessioned 2020-12-24T19:30:42Z
dc.date.available 2020-12-24T19:30:42Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70922
dc.description.abstract Context factors have lasting impacts on people’s sentiments. Exploring impacts that different contexts have on sentiments can be crucial for managing the increasing number of communications companies nowadays maintain with customers via social media channels. To help companies prevent impacts of negative word of mouth, we provide an overview about sentiment-influential contexts for tweets as one kind of social media texts previously discussed within the literature. We collected an overall amount of 358.923.210 tweets and performed analysis to uncover the effects of continents, mobile devices’ operating systems (OS) and the combination of both on sentiments expressed within tweets. Our results show remarkable differences for tweets originating from North America and Apple devices, which turned out to be the tweets with the lowest sentiments compared to the other continents and the mobile OS Android.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data-driven Social Media Management
dc.subject contexts
dc.subject continents
dc.subject mobile devices' operation systems
dc.subject sentiment-influential factors
dc.subject tweets
dc.title Identifying Sentiment Influences Provoked by Context Factors – Results from a Data Analytics Procedure Performed on Tweets
dc.identifier.doi 10.24251/HICSS.2021.308
prism.startingpage 2511
Appears in Collections: Big Data-driven Social Media Management


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