Schwaiger, Josef and Hammerl, Timo and Florian, Johannsen and Leist, Susanne (2021) UR: SMART-A tool for analyzing social media content. INFORMATION SYSTEMS AND E-BUSINESS MANAGEMENT, 19 (4). pp. 1275-1320. ISSN 1617-9846, 1617-9854
Full text not available from this repository. (Request a copy)Abstract
The digital transformation, with its ongoing trend towards electronic business, confronts companies with increasingly growing amounts of data which have to be processed, stored and analyzed. Instant access to the "right" information at the time it is needed is crucial and thus, the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage. In this context, one important field of application is digital marketing, because sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior based on their reviews, complaints as well as posts in online forums or social networks. However, existing tools for the automated analysis of social content often focus on one general approach by either prioritizing the analysis of the posts' semantics or the analysis of pure numbers (e.g., sum of likes or shares). Hence, this design science research project develops the software tool UR:SMART, which supports the analysis of social media data by combining different kinds of analysis methods. This allows deep insights into users' needs and opinions and therefore prepares the ground for the further interpretation of the voice. The applicability of UR:SMART is demonstrated at a German financial institution. Furthermore, the usability is evaluated with the help of a SUMI (Software Usability Measurement Inventory) study, which shows the tool's usefulness to support social media analyses from the users' perspective.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | DESIGN SCIENCE RESEARCH; MULTINOMIAL NAIVE BAYES; SENTIMENT ANALYSIS; BIG DATA; METHODOLOGY; ANALYTICS; CLASSIFICATION; CHALLENGES; KNOWLEDGE; USABILITY; Social media analysis; Sentiment analysis; Classification; Mixed method approach |
| Subjects: | 300 Social sciences > 330 Economics |
| Divisions: | Business, Economics and Information Systems > Institut für Wirtschaftsinformatik > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) Informatics and Data Science > Lehrstuhl für Wirtschaftsinformatik III - Business Engineering (Prof. Dr. Susanne Leist) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 25 Aug 2022 05:56 |
| Last Modified: | 25 Aug 2022 05:56 |
| URI: | https://pred.uni-regensburg.de/id/eprint/46964 |
Actions (login required)
 |
View Item |
