Purpose

This 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/approach

The 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.

Findings

The 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/value

First, 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.

中文翻译：

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使用机器学习捕捉来自六个国家的 COVID-19 消费者情绪：方法说明

目的

本文旨在说明在国际营销中使用计算机辅助内容分析的范围和挑战，旨在从多语言推文中捕捉消费者对 COVID-19 的情绪。

设计/方法/方法

该研究基于大约 3500 万条与 COVID-19 相关的原始推文。该研究方法说明了使用监督机器学习和人工神经网络技术进行广泛的信息提取。

发现

作者确定了来自六个国家的超过 200 万条推文，并将它们分类为 PESTEL（即政治、经济、社会、技术、环境和法律）维度。提取的消费者情绪和相关情绪显示出各国之间的巨大差异。我们的分析突出了在国际营销中使用多语言在线情绪分析所固有的机遇和挑战。基于这些见解，提出了几个未来的研究方向。

原创性/价值

首先，作者通过为多语言环境中的计算机辅助文本挖掘提供“用例”，为国际营销方法的开发做出了贡献。其次，作者增加了关于不同国家与 COVID-19 相关的消费者情绪差异的知识。第三，作者为非结构化多媒体帖子分析的未来研究提供了途径。