Emotion Prediction of Paragraph using Big Data Analysis

빅데이터 분석을 이용한 문단 내의 감정 예측

  • Kim, Jin-su (College of Liberal Arts, Anyang University)
  • Received : 2016.09.30
  • Accepted : 2016.11.20
  • Published : 2016.11.28


Creation and Sharing of information which is structured data as well as various unstructured data. makes progress actively through the spread of mobile. Recently, Big Data extracts the semantic information from SNS and data mining is one of the big data technique. Especially, the general emotion analysis that expresses the collective intelligence of the masses is utilized using large and a variety of materials. In this paper, we propose the emotion prediction system architecture which extracts the significant keywords from social network paragraphs using n-gram and Korean morphological analyzer, and predicts the emotion using SVM and these extracted emotion features. The proposed system showed 82.25% more improved recall rate in average than previous systems and it will help extract the semantic keyword using morphological analysis.


Emotion Prediction;Korean Morphological Analyzer;Modified n-gram;Support Vector Machine(SVM);Association Rule;Sequential Pattern


  1. Sung-hyun Yun, Keun-ho Lee, Heui-seok Lim, Dae-ryong Kim, Jung-hoon Kim, "The Method of Digital Copyright Authentication for Contents of Collective Intelligence", Journal of the Korea Convergence Society, Vol. 6, No. 6, pp. 185-193, 2015.
  2. Jung-Hoon Kim, Jun-Young Go, Keun-Ho Lee, "A Scheme of Social Engineering Attacks and Countermeasures Using Big Data based Conversion Voice Phishing", Journal of the Korea Convergence Society, Vol. 6, No. 1, pp. 85-91, 2015.
  3. Dong-Yup Choi, Jin-Kyu Park, Tae-Jung Kim, "An Emotion Extraction Method from SMS Text for the Emotion Expression Robot", Journal of Korea Intellectual Patent Society, Vol.18, No. 44, pp.5-8, 2016.
  4. Young-Seok Yoo, Bang-Yong, Sohn, "Music Listening Behavior analysis of Twitter User and A Comparative Study of Domestic Music Ranking", Journal of Digital Convergence, Vol.14, No.5, pp.309-316, 2016.
  5. Eun-Jin Jung, Joo-Chang Kim, Joo-Chang Kim, Kyungyong Chung, "Social Network based Sensibility Design Recommendation using {User - Associative Design} Matrix", Journal of Digital Convergence, Vol.14, No.8, pp.313-318, 2016.
  6. Michael W Morris, Dacher Keltner. "How Emotions Work: the Social Functions of Emotional Expression in Negotiatios", Research in Organizational Behavior, 22, pp.1-50, 2000.
  7. Robert E. Thayer, "The Biopsychology of Mood and Arousal", Oxford University Press, 1989.
  8. HeeSam Shin, The Society Of Korean Semantics, Korean Semantics 15, pp. 207-225, 2004.
  10. Bernhard E Boser, Isabelle M Guyon, and Vladimir N Vapnik, "A Training Algorithm for Optimal Margin Classifiers", Proc. The Fifth Annual Workshop on Computational Learning Theory, pp.144-152, 1992..
  11. John Gantz, David Reinsel, "Extracting Value from Chaos", IDC IVIEW June, p.6, 2011.
  12. O'Reilly Radar Team, Planning for Big Data, O'Reilly, 2012.
  14. Jin-Su Kim, "Emotion Prediction of Document using Paragraph Analysis", Journal of Digital Convergence, Vol. 12, No. 12, pp.249-255, 2014.
  16. Do,H. H., Melnik, S. & Rahm, E. 2002. Comparison of Schema Matching Evaluations. In Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems, Akmal B. Chaudhri, Mario Jeckle, Erhard Rahm, and Rainer Unland (Eds.). Springer-Verlag, London, UK, 221-237.

Cited by

  1. Associative Feature Information Extraction Using Text Mining from Health Big Data pp.1572-834X, 2018,