UR: SMART–A tool for analyzing social media content

Josef Schwaiger, Timo Hammerl, Johannsen Florian () and Susanne Leist
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Josef Schwaiger: University of Regensburg
Timo Hammerl: University of Regensburg
Johannsen Florian: University of Applied Sciences
Susanne Leist: University of Regensburg

Information Systems and e-Business Management, 2021, vol. 19, issue 4, No 6, 1275-1320

Abstract: 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.

Keywords: Social media analysis; Sentiment analysis; Classification; Mixed method approach (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10257-021-00541-4

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