Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287381 
Year of Publication: 
2021
Citation: 
[Journal:] Information Systems Frontiers [ISSN:] 1572-9419 [Volume:] 24 [Issue:] 5 [Publisher:] Springer US [Place:] New York, NY [Year:] 2021 [Pages:] 1635-1646
Publisher: 
Springer US, New York, NY
Abstract: 
Social media enable companies to assess consumers' opinions, complaints and needs. The systematic and data-driven analysis of social media to generate business value is summarized under the term Social Media Analytics which includes statistical, network-based and language-based approaches. We focus on textual data and investigate which conversation topics arise during the time of a new product introduction on Twitter and how the overall sentiment is during and after the event. The analysis via Natural Language Processing tools is conducted in two languages and four different countries, such that cultural differences in the tonality and customer needs can be identified for the product. Different methods of sentiment analysis and topic modeling are compared to identify the usability in social media and in the respective languages English and German. Furthermore, we illustrate the importance of preprocessing steps when applying these methods and identify relevant product insights.
Subjects: 
Social media analytics
Natural language processing
Topic modeling
Sentiment analysis
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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