Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
권호 표지
DOI QR코드

DOI QR Code

Design of Big Data Preference Analysis System

빅데이터 선호도 분석 시스템 설계

  • Son, Sung Il (Department of Computer & Information Engineering Graduate School, Cheongju University) ;
  • Park, Chan Khon (Department of Computer & Information Engineering Graduate School, Cheongju University)
  • Received : 2014.07.16
  • Accepted : 2014.10.02
  • Published : 2014.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper suggests the way that it could improve the reliability about preference of user's feedback by adding weighting factor on sentiment analysis, and efficiently make a sentiment analysis of users' emotional perspective on the big data massively generated on twitter. To solve errors on earlier studies, this paper has improved recall and precision of sensibility determination by using sensibility dictionary subdivided sentiment polarity based on the level of sensibility and given impotance to sensibility determination by populating slang, new words, emoticons and idiomatic expressions not in the system dictionary. It has considered the context through conjunctive adverbs fixed in korean characteristics which are free to the word order. It also recognize sensibility words such as TF(Term Frequency), RT(Retweet), Follower which are weighting factors of preference and has increased reliability of preference analysis considering weight on 'a very emotional tweet', 'a recognised tweet from users' and 'a tweeter influencer'

Keywords

References

  1. S.C. Yoon, H. Namgung, S.K. Yang, and H.K. Kim, "Big Data Driven Semantic Web Technology Trends large," The Korean Institute of Communications and Information Sciences, Vol, 29, No. 11, pp. 24-29, 2013.
  2. C.H. Lee, J. Her, H.J. Oh, H.J. Kim, B.M. Ryu, and H.K. Kim, "Technology Trends of Issue Detection and Predictive Analysis on Social," Korean Institute of Information Scientists and Engineers, Vol. 30, No. 6, pp. 47-58, 2012.
  3. S.J. Im and O.K. Min, "Machine Learning Technology Trends for Big Data Processing," Electronics and Telecommunications Trends, Vol, 2012, No. 4, pp. 55-63, 2012.
  4. Y.S. Kim, News Big Data Opinion Mining Model for Predicting KOSPI Movement, Ph.D. Thesis of Kookmin University, 2012.
  5. Kushal Dave, Steve Lawrence, and David M. Pennock, "Mining the Peanut Gallery : Opinion Extraction and Semantic Classification of Product Reviews," Proceedings of International Conference on World Wide Web, pp. 519-523, 2003.
  6. J.H. Han, "How Do We Define Korean Subject : a Typological Perspective, Focusing on Predicate Type-centered Definition of Korean Subject," The Association for Korean Linguistics, Vol. 60, pp. 189-225, 2013.
  7. S.H. Han, "Study on Use of Korean Conjunctive adverbs," Institute of Language and Information Studies, Yonsei University Language Facts and Perspectives, Vol. 31, pp. 139-169, 2013.
  8. T.W. Seo, M.G. Park, and C.S. Kim, "Design and Implementation of the Extraction Mashup for Reported Disaster Information on SNSs," Journal of Korea Multimedia Society, Vol. 16, No. 1, pp. 1297-1304, 2013. https://doi.org/10.9717/kmms.2013.16.11.1297

Cited by

  1. Development of Smart Healthcare Wear System for Acquiring Vital Signs and Monitoring Personal Health vol.19, pp.5, 2016, https://doi.org/10.9717/kmms.2016.19.5.808
  2. IoT 및 위치 추적 기술 기반의 양액 순환 방식을 활용한 작물의 최적 생장 관리 시스템에 관한 연구 vol.19, pp.11, 2014, https://doi.org/10.9717/kmms.2016.19.11.1891
  3. Scene Arrangement Analyzed through Data Visualization of Climax Patterns of Films vol.18, pp.8, 2017, https://doi.org/10.9728/dcs.2017.18.8.1621
  4. e-Commerce 상에서 빅데이터 서비스제공 기대가 이용의도에 미치는 영향 연구 vol.22, pp.9, 2019, https://doi.org/10.9717/kmms.2019.22.9.1132