Abstract
Many of the texts posted on Twitter are broken sentences, and the translated sentences may not be accurate. An inaccurate translation may spoil the meaning of the original text and induce miscommunication between the poster and the reader who uses the machine translation. Since many sentences tweeted on Twitter contain emotional expressions, this study uses sentiment analysis to calculate and compare the sentiment scores of the original and translated sentences to investigate the change in sentiment before and after machine translation. As a result of using dictionaries to classify tweets before and after translation, it was found that the classification of positive sentences tended to be more likely the same before and after translation. In addition, the results of the sentiment analysis of “joy”, “like”, “relief” and “excitement” by machine learning showed that the sentiment of “joy” was particularly increased when translated from Japanese into English.
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Fukuda, K., Jin, Q. (2022). Analyzing Change on Emotion Scores of Tweets Before and After Machine Translation. In: Meiselwitz, G. (eds) Social Computing and Social Media: Design, User Experience and Impact. HCII 2022. Lecture Notes in Computer Science, vol 13315. Springer, Cham. https://doi.org/10.1007/978-3-031-05061-9_21
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