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A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter

Author

Listed:
  • Irina Wedel

    (University of Hamburg)

  • Michael Palk

    (University of Hamburg)

  • Stefan Voß

    (University of Hamburg)

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.

Suggested Citation

  • Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    • Handle: RePEc:spr:infosf:v:24:y:2022:i:5:d:10.1007_s10796-021-10169-x
    DOI: 10.1007/s10796-021-10169-x
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    References listed on IDEAS

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    1. Sebastián Vallejos & Diego G. Alonso & Brian Caimmi & Luis Berdun & Marcelo G. Armentano & Álvaro Soria, 2021. "Mining Social Networks to Detect Traffic Incidents," Information Systems Frontiers, Springer, vol. 23(1), pages 115-134, February.
    2. Kawaljeet Kaur Kapoor & Kuttimani Tamilmani & Nripendra P. Rana & Pushp Patil & Yogesh K. Dwivedi & Sridhar Nerur, 2018. "Advances in Social Media Research: Past, Present and Future," Information Systems Frontiers, Springer, vol. 20(3), pages 531-558, June.
    3. Reema Aswani & Arpan Kumar Kar & P. Vigneswara Ilavarasan, 2018. "Detection of Spammers in Twitter marketing: A Hybrid Approach Using Social Media Analytics and Bio Inspired Computing," Information Systems Frontiers, Springer, vol. 20(3), pages 515-530, June.
    4. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    5. Wei-Lun Chang & Yi-Pei Chen, 2019. "Way too sentimental? a credible model for online reviews," Information Systems Frontiers, Springer, vol. 21(2), pages 453-468, April.
    6. Ajaya Kumar Swain & Ray Qing Cao, 2019. "Using sentiment analysis to improve supply chain intelligence," Information Systems Frontiers, Springer, vol. 21(2), pages 469-484, April.
    7. Marc Cheong & Vincent C. S. Lee, 2011. "A microblogging-based approach to terrorism informatics: Exploration and chronicling civilian sentiment and response to terrorism events via Twitter," Information Systems Frontiers, Springer, vol. 13(1), pages 45-59, March.
    8. Stieglitz, Stefan & Mirbabaie, Milad & Ross, Björn & Neuberger, Christoph, 2018. "Social media analytics – Challenges in topic discovery, data collection, and data preparation," International Journal of Information Management, Elsevier, vol. 39(C), pages 156-168.
    9. N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.
    10. Ashish K. Rathore & Arpan K. Kar & P. Vigneswara Ilavarasan, 2017. "Social Media Analytics: Literature Review and Directions for Future Research," Decision Analysis, INFORMS, vol. 14(4), pages 229-249, December.
    11. Funda Ustek-Spilda & Davide Vega & Matteo Magnani & Luca Rossi & Irina Shklovski & Sebastian Lehuede & Alison Powell, 2021. "A Twitter-Based Study of the European Internet of Things," Information Systems Frontiers, Springer, vol. 23(1), pages 135-149, February.
    12. Gaganmeet Kaur Awal & K. K. Bharadwaj, 2019. "Leveraging collective intelligence for behavioral prediction in signed social networks through evolutionary approach," Information Systems Frontiers, Springer, vol. 21(2), pages 417-439, April.
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