- Volume 14 Issue 3
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Differences in Sentiment on SNS: Comparison among Six Languages
SNS에서의 언어 간 감성 차이 연구: 6개 언어를 중심으로
- Kim, Hyung-Ho (Dept. of Information & Logistics of Sehan University) ;
- Jang, Phil-Sik (Dept. of Information & Logistics of Sehan University)
- Received : 2016.02.01
- Accepted : 2016.03.20
- Published : 2016.03.28
The purpose of this study was to explore the differences in sentiment on social networking sites among six languages (English, German, Russian, Spanish, Turkish and Dutch). A total of 204 million tweets were collected using Streaming API. Subjective/objective ratio, sentiment strength, positive/negative ratio, number of retweets and boundary impermeability were analyzed with SentiStrength to estimate the trends of emotional expression via Twitter. The results showed that subjective/objective ratio and the positive/negative ratio of tweets were significantly different by languages (p<0.001). And, there were significant effects of language on sentiment strength, boundary impermeability and the number of retweets (p<0.001). The results also indicate that the cross-cultural, language differences should be taken into account in sentiment analysis on SNS.
Supported by : 세한대학교
- J. J. Yoo, D. Kim and J. Moon, "Exploring cross-cultural differences in self-presentation and self-disclosure in social Networking sites: A comparison of korean and american SNS users", Journal of Advertising and Promotion Research, Vol. 1, No. 2, pp. 77-118, 2012.
- S. Luo, "Cross-cultural differences between American and Chinese college students on self-disclosure on social media. Graduate Theses and Dissertations", p.1-70, Iowa State University. 2014.
- K. Omori and M. Allen, "Cultural differences between american and japanese self-presentation on SNSs", International Journal of Interactive Communication Systems and Technologies (IJICST), Vol. 4, Issue 1. DOI: 10.4018/ijicst.2014010104, 2014. https://doi.org/10.4018/ijicst.2014010104
- S. A. Golder and M. W. Macy, "Diurnal and seasonal mood vary with work, sleep and daylength across diverse cultures." Science, 333, pp. 1878-1881, 2011. https://doi.org/10.1126/science.1202775
- J. Y. Lee, P. S. Jang, "Effects of message polarity and type on word of mouth through SNS (Social Network Service)", The Journal of Digital Policy & Management, Vol. 11, No. 6, pp. 129-135, 2013.
- K. Choi, J. A. Yoo, "A reviews on the social network analysis using R", Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 77-83, 2015. https://doi.org/10.15207/JKCS.2015.6.1.077
- J. Y. Go, K. H. Lee, "SNS disclosure of personal information in M2M environment threats and countermeasures", Journal of the Korea Convergence Society, Vol. 5, No. 1, pp. 29-34, 2014. https://doi.org/10.15207/JKCS.2014.5.1.029
- B. Pang, and L. Lee, "Opinion mining and sentiment analysis", Foundations and Trends in Information Retrieval, Vol. 1. No. 2, pp. 1-135, 2008.
- P. S. Jang, "Study on principal sentiment analysis of social data", Journal of The Korea Society of Computer and Information, Vol. 19, No. 12, pp.49-56, 2014.
- D. M. Boyd and N. B. Ellison, N. B. "Social network sites: Definition, history, and scholarship", Journal of Computer-Mediated Communication, Vol. 13, No. 1. pp. 210-230, 2007 https://doi.org/10.1111/j.1083-6101.2007.00393.x
- S. C. Walton and R. E. Rice, "Mediated disclosure on Twitter: The roles of gender and identity in boundary impermeability, valence, disclosure, and stage", Computers in Human Behavior, Vol. 29, pp. 1465-1474, 2013. https://doi.org/10.1016/j.chb.2013.01.033
- "The Streaming APIs", (C) 2016 Twitter, Inc., https://dev.twitter.com/streaming/overview (Jan 5, 2016)
- "MongoDB 3.2", MongoDB, Inc., https://www.mongodb.org/ (Jan 5, 2016)
- M. Thelwall, K. Buckley. & G. Paltoglou, "Sentiment strength detection for the social Web", Journal of the American Society for Information Science and Technology, Vol. 63, No. 1, pp. 163-173, 2012. https://doi.org/10.1002/asi.21662
- "SentiStrength", http://sentistrength.wlv.ac.uk/, (Jan 25, 2016)