Employing machine learning for capturing COVID-19 consumer sentiments from six countries: a methodological illustration

PurposeThis paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.Design/methodology/approachThe study is based on some 35 million original COVID-19-re...

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Bibliographic Details
Published in:	International marketing review Vol. 40; no. 5; pp. 869 - 893
Main Authors:	Schlegelmilch, Bodo B., Sharma, Kirti, Garg, Sambbhav
Format:	Journal Article
Language:	English
Published:	London Emerald Publishing Limited 12-12-2023 Emerald Group Publishing Limited
Subjects:	Algorithms Consumers COVID-19 Data mining Dictionaries Emotions Foreign language learning Language Machine learning Marketing Natural language Pandemics Sentiment analysis Social networks User generated content Virtual communities Text mining Supervised machine learning Multi-media posts Consumer sentiments PESTEL Artificial neural network Multi-lingual tweets Computer-aided analysis Cross-country comparisons
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Summary:	PurposeThis paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets.Design/methodology/approachThe study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction.FindingsThe authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed.Originality/valueFirst, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.
ISSN:	0265-1335 1758-6763
DOI:	10.1108/IMR-06-2021-0194