Identifying Sentiment Influences Provoked by Context Factors – Results from a Data Analytics Procedure Performed on Tweets
| 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.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.identifier.doi | https://doi.org/10.24251/HICSS.2021.308 | |
| dc.identifier.isbn | 978-0-9981331-4-0 | |
| dc.identifier.uri | http://hdl.handle.net/10125/70922 | |
| 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 | |
| prism.startingpage | 2511 |
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