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Identifying Sentiment Influences Provoked by Context Factors – Results from a Data Analytics Procedure Performed on Tweets
Book section
Konadl, Daniel, Wörner, Janik and Leist, Susanne
(2021)
Identifying Sentiment Influences Provoked by Context Factors – Results from a Data Analytics Procedure Performed on Tweets.
In:
Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021).
University of Hawai'i at Manoa, Honolulu, HI, pp. 2511-2520.
ISBN 978-0-9981331-4-0.
DOI to cite this document: 10.5283/epub.44358
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 neg-ative word of mouth, we provide an overview about sentiment-influential contexts for tweets as one ...
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 neg-ative 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 un-cover the effects of continents, mobile devices’ oper-ating 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.
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Details
| Item type | Book section |
| ISBN | 978-0-9981331-4-0 |
| Title of Book | Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021) |
| Publisher | University of Hawai'i at Manoa |
| Place of Publication | Honolulu, HI |
| Page Range | pp. 2511-2520 |
| Date | 5 January 2021 |
| Date of publication | 07 Jan 2021 06:48 |
| Institutions | Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) Informatics and Data Science > Department Information Systems > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) |
| Dewey Decimal Classification | 000 Computer science, information & general works > 004 Computer science |
| Status | Published |
| Refereed | Yes, this version has been refereed |
| Created at the University of Regensburg | Yes |
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-443581 |
| Item ID | 44358 |
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