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Based on SNS data, this study analyzed the differences in words, topics, and the similarity of words in the keywords related to the Summer Olympics from the 2008 Beijing Olympics to the 2020 Tokyo Olympics. The following conclusions were drawn through the analysis of Term Frequency-Inverse Document Frequency(TF-IDF), Topic Modeling, and Word Embedding. First, the words that applied TF-IDF technique to the words that appeared from the 2008 Beijing Olympics to the 2016 Rio Olympics showed differences from year to year. It was difficult to find common words by year.This shows that the trend changes due to the frequency difference of the most frequently used words. second, child Topics that emerged from the 2008 Beijing Olympics to the 2016 Rio Olympics were different from year to year. The annual topics were able to infer sports trends. Third, Similar words with high TF-IDF values differed from year to year in the words that appeared from the 2008 Beijing Olympics to the 2016 Rio Olympics. We could sort out what sentences similar words were describing. A semantic association could be given to extracted keywords. Thus, in conclusion, through the application of TF-IDF, Topic Modeling, and Word Embedding in the summer Olympic related keyword trend analysis study, the differences in words appearing year by year, differences in topics appearing, and differences in similarities in words appearing. It has been shown that sports trends can be checked with changes in keywords over time.
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