UR: SMART\u2013A tool for analyzing social media content

Josef Schwaiger,Johannsen Florian,Timo Hammerl,Susanne Leist

doi:10.1007/s10257-021-00541-4

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.

Highlights

Introduction and MotivationThe digital transformation, with the rise of new technologies such as “CyberPhysical-Systems (CPS)”, “Virtual Reality”, “3-D Printing” and “Auto-IDTechniques” (Hänisch 2017), and the ongoing trend towards electronic business confront companies with increasingly growing amounts of data that have to be processed, stored and analyzed (CapGemini 2020; Fill and Johannsen 2016)
We focus on sentiment analysis, classification and clustering in particular, because their effectiveness has been proven in practice (e.g., Alalwan et al 2017) and they can be purposefully combined to come to a mixed method approach (e.g., Stieglitz et al 2018) that enables social media data analysis from different angles, which is the research objective of this study
Based on real-world problems as well as characteristics concerning the analysis of social media data, which we identified by interviewing practitioners, beneficial scenarios for a purposeful combination of different analysis methods were introduced

Summary

Introduction

Introduction and MotivationThe digital transformation, with the rise of new technologies such as “CyberPhysical-Systems (CPS)”, “Virtual Reality”, “3-D Printing” and “Auto-IDTechniques” (Hänisch 2017), and the ongoing trend towards electronic business confront companies with increasingly growing amounts of data that have to be processed, stored and analyzed (CapGemini 2020; Fill and Johannsen 2016). Instant access to the “right” information at the time it is needed is crucial and the use of techniques for the handling of big amounts of unstructured data, in particular, becomes a competitive advantage (Bali et al 2017; Fill and Johannsen 2016; Grover et al 2018; Hwang 2019) This is true for so called knowledge-intensive business areas where the processing and analysis of big data become highly relevant. In this context, one important field of application is digital marketing (Chaffey and Ellis-Chadwick 2019) because a sophisticated data analysis allows companies to gain deeper insights into customer needs and behavior (Kitchens et al 2018; Schwaiger et al 2017). Social media data can be exploited by IT-based data analytics, e.g., for the purpose of market analysis or campaign planning straight away (Hwang 2019; Malthouse et al 2013; Stieglitz et al 2014; Trainor et al 2014)

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