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Computational political communication, based on big data analytics of social media texts, provides a paradigm for understanding the public's view of and engagement with political events worldwide. This study reviews previous efforts by social and data scientists and offers a demo to show the potential of computational political communication. To characterize online political communication dynamics surrounding U.S.-China tensions and gain a better understanding of the U.S.-China power struggle, a vast amount of user-generated Twitter data is compiled from March 2020 to March 2021 globally. Chinese IT giants (Huawei, Tencent, and ByteDance) and major English-speaking countries (the United States, United Kingdom, Canada, Australia, New Zealand, India, and Pakistan) are chosen as keywords for filtering the tweets gathered. Sentiment analysis of the tweets is carried out automatically. It is found that the popularities of debates regarding certain nations and companies are uneven and might be triggered by events. Furthermore, rather than being segregated, the discourses of all of these companies are intertwined. It is expected that future studies can apply more fine-grained, categorized, and automated sentiment and topic analysis to show a panorama of online public opinion. [ABSTRACT FROM AUTHOR]