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MANTRA: A Topic Modeling-Based Tool to Support Automated Trend Analysis on Unstructured Social Media Data
Wörner, Janik, Konadl, Daniel
, Leist, Susanne
und Schmid, Isabel
(2023)
MANTRA: A Topic Modeling-Based Tool to Support Automated Trend Analysis on Unstructured Social Media Data.
In: 44. International Conference on Information Systems (ICIS), 10.12.2023 bis 13.12.2023, Hyderabad, Indien.
Veröffentlichungsdatum dieses Volltextes: 20 Nov 2023 09:37
Konferenz- oder Workshop-Beitrag
DOI zum Zitieren dieses Dokuments: 10.5283/epub.55025
Zusammenfassung
The early identification of new and auspicious ideas leads to competitive advantages for companies. Thereby, topic modeling can serve as an effective analytical approach for the automated investigation of trends from unstructured social media data. However, existing trend analysis tools do not meet the requirements regarding (a) Product Development, (b) Customer Behavior Analysis, and (c) ...
The early identification of new and auspicious ideas leads to competitive advantages for companies. Thereby, topic modeling can serve as an effective analytical approach for the automated investigation of trends from unstructured social media data. However, existing trend analysis tools do not meet the requirements regarding (a) Product Development, (b) Customer Behavior Analysis, and (c) Market-/Brand-Monitoring as reflected within extant literature. Thus, based on the requirements for each of these common marketing-related use cases, we derived design principles following design science research and instantiated the artifact “MANTRA” (MArketiNg TRend Analysis). We demonstrated MANTRA on a real-world data set (~1.03 million Yelp reviews) and hereby could confirm remarkable trends of vegan and global cuisine. In particular, the importance of meeting all specific requirements of the respective use cases and especially flexibly incorporating several external parameters into the trend analysis is exemplified.
Beteiligte Einrichtungen
Details
| Dokumentenart | Konferenz- oder Workshop-Beitrag (Paper) |
| Datum | 10 Dezember 2023 |
| Institutionen | Wirtschaftswissenschaften > Institut für Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) Informatik und Data Science > Fachbereich Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) |
| Stichwörter / Keywords | Social Media Analytics, Trend Analysis, Topic Modeling-Based Tool, Design Science, Marketing |
| Dewey-Dezimal-Klassifikation | 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik |
| Status | Veröffentlicht |
| Begutachtet | Ja, diese Version wurde begutachtet |
| An der Universität Regensburg entstanden | Ja |
| Dokumenten-ID | 55025 |
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