Analysis on Smartphone Related Twitter Reviews by Using Opinion Mining Techniques

  • Jeongin Kim
  • Dongjin Choi
  • Myunggwon Hwang
  • Pankoo Kim
Part of the Studies in Computational Intelligence book series (SCI, volume 551)


Due to the recent popularization of smartphones, it has become easy to get connected on Social Network Service (SNS) which caused the proliferation on the amount of tweets on Twitter. Many studies have been proposed to discover valuable meanings from Twitter text messages by using opinion mining. However, these researches have a side effect that it is only focused on positive and negative in the limited category. In this paper, we will attempt to examine which factors could affect the users interests or preferences by analyzing and comparing smartphone product reviews which were posted on Twitter, in multilateral categories, by using opinion mining.


Social Network Services Opinion Mining Twitter 


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  1. 1.
    Marketresearch on digital media, internet marketing,
  2. 2.
    Kang, J., Kim, C., Yoo, S., Lee, Y., Park, H., Kim, Y.: Recommendation of Acquaintances to Follow in Twitter. In: The 38th Domestic Conference on Korean Institute of Information Scientists and Engineers, Korea, vol. 38(2), pp. 383–386 (2011)Google Scholar
  3. 3.
    Lee, G., Lee, E., Kwak, K., Park, J., Je, R., Kim, J.: A study on charactheristics of Twitter users (compared to Cyworld, Facebook). In: HCI 2011, pp. 1043–1050 (2011)Google Scholar
  4. 4.
    Kim, J., Ko, B., Jeong, H., Kim, P.: A Method for Extracting Topics in News Twitter. 7th International Journal of Software Engineering and Its Applications 7(2), 1–6 (2013)Google Scholar
  5. 5.
    Lee, J., Kim, C.: Analysis of the Information Diffusion Process on Twitter:Effects of Influentials and Hyperlinks. Korean Journal of Journalism & Communication Studies 56(3), 238–265 (2012)Google Scholar
  6. 6.
    Hwang, M., Choi, D., Kim, P.: A Context Information Extraction Method according to Subject for Semantic Text Processing. Journal of Korean Institute of Information Technology 8(11), 197–204 (2010)Google Scholar
  7. 7.
    Hwang, M., Kim, P.: An Enrichment Method on Semantic Relation Network of WordNet. Journal of Korean Institute of Information Technology 7(5), 209–215 (2009)Google Scholar
  8. 8.
    Park, K., Hwang, K.: A Bio-Text Mining System Based on Natural Language Processing. Journal of KISS: Computing Practices 17(4), 205–213 (2011)MathSciNetGoogle Scholar
  9. 9.
    Choi, Y., Park, S.: Interplay of Text Mining and Data Mining for Classifying Web Contents. Korean Journal of Cognitive Science 13(3), 33–46 (2002)MathSciNetGoogle Scholar
  10. 10.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  11. 11.
    Yang, J., Myung, J., Lee, S.: A Sentiment Classification Method Using Context Information in Product Review Summarization. Journal of KISS: Database 36(4), 254–262 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jeongin Kim
    • 1
  • Dongjin Choi
    • 1
  • Myunggwon Hwang
    • 2
  • Pankoo Kim
    • 1
  1. 1.Dept. of Computer EngineeringChosun UniversityGwangjuRepublic of Korea
  2. 2.Korea Institute of Science and Technology Institute (KISTI)DaejeonRepublic of Korea

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