TY - GEN
T1 - Analyzing Change on Emotion Scores of Tweets Before and After Machine Translation
AU - Fukuda, Karin
AU - Jin, Qun
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - BERT
KW - Emotion score
KW - Machine translation
KW - SNS
KW - Sentiment analysis
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85133013565&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85133013565&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05061-9_21
DO - 10.1007/978-3-031-05061-9_21
M3 - Conference contribution
AN - SCOPUS:85133013565
SN - 9783031050602
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 308
BT - Social Computing and Social Media
A2 - Meiselwitz, Gabriele
PB - Springer Science and Business Media Deutschland GmbH
T2 - 14th International Conference on Social Computing and Social Media, SCSM 2022 Held as Part of the 24th HCI International Conference, HCII 2022
Y2 - 26 June 2022 through 1 July 2022
ER -