The Sensitivity Analysis for Customer Feedback on Social Media

소셜 미디어 상 고객피드백을 위한 감성분석

  • Received : 2015.01.14
  • Accepted : 2015.02.24
  • Published : 2015.04.30


Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.


Social media;Big Data;Customer feedback;Sensitivity analysis;Morphological analysis


Supported by : 남서울대학교


  1. K.Cheong, H.Y. Seo and S.D. Cho," Classifications and Content Analyses of Social Networking Services Research", Journal of The Korean Knowledge Information Technology Society vol. 6, no.5, pp.82-98, 2011.
  2. K.P. Nam, "System Implementation of the Customer Satisfaction Survey Using Internet", The Korean Journal of Applied Statistics vol.18 , no.3 , pp.713-727, 2005.
  3. James Manyika, "Big Data: The next frontier for innovation, competition, and productivity", McKinsey Global Institute Report , May 2011.
  4. S.J. Kim and Y.S. Kim, "Design and Implementation of Good Bibimbap Restaurant Recommendation System Using TCA based on BigData", International Journal of Software Engineering and Its Applications, vol. 8, no.7, pp. 95-106, 2014.
  5. E. J. Choi and S. H. Kim, "The Study of the Impact of Perceived Quality and Value of Social Enterprises on Customer Satisfaction and Re-Purchase Intention", International Journal of Smart Home (IJSH), vol. 7, no.1, pp. 239-252, 2013.
  6. S.Y. Park, J.O. Chang and T.S. Kihl, "Document Classification Model Using Web Documents for Balancing Training Corpus Size per Category" Journal of information and communication convergence engineering vol.11 n.4, pp.268-273, 2013.
  7. J.M.Park, " Gene Algorithm of Crowd System of Data Mining", Journal of Information and Communication. Convergence Engineering (JICCE) vol. 10, no.1, pp.40-44, 2012.
  8. K.S. Shim and J.H. Yang, "High Speed Korean Morphological Analysis based on Adjacency Condition Check" Journal of KIISE : Software and Applications, Vol. 31, No. 1, pp. 89-99, 2014.
  9. E.J Song, "A Study on the Collection Site Profiling and Issue-detection Methodology for Analysis of Customer Feedback on Social Big Data", International Journal of Smart Home (IJSH) Vol. 8, No. 6 pp. 169-178,2014.
  10. Y.J. Moon "The Effect of Individual Differences on Consumer satisfaction and Behavioral Intention in Online Shopping: The Role of Information Privacy Concerns", Journal of the Korea Institute of Information and Communication Engineering(KIICE), vol.17, no.11, 2717-2722, 2013.
  11. S. Abduljalil and D.K. Kang "Legacy of Smart Device, Social Network and Ubiquitous E-class System" Journal of information and communication convergence engineering (JICCE), vol.9, no.1, pp.1-5, 2011.

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