Abstract
The global live-streaming market was significantly impacted by the COVID-19 pandemic in the first half of 2020. In this study, we aim to analyze the current state of the live-streaming shopping market in the US from the outbreak's onset to the present, i.e., 2022. In our research, our primary core approach is around the modeling of machine learning clustering. First, we used R language to get data from Twitter, the mainstream social media in the US, then used common word text mining to analyze 5000 tweets. After several trials and analyses, we confirmed the division of eight subgroups and three large groups (platforms, live beauty streaming, and blockchain virtual currency) after the final data analysis session. These specific groups demonstrate which parts of Livestream shopping are of particular appeal to American customers, providing other marketing ideas for merchants and platform partners.
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