본문 바로가기
  • Home

Study on Principal Sentiment Analysis of Social Data

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2014, 19(12), pp.49-56
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Phil-Sik Jang 1

1세한대학교

Accredited

ABSTRACT

In this paper, we propose a method for identifying hidden principal sentiments among large scale textsfrom documents, social data, internet and blogs by analyzing standard language, slangs, argots,abbreviations and emoticons in those words. The IRLBA(Implicitly Restarted Lanczos BidiagonalizationAlgorithm) is used for principal component analysis with large scale sparse matrix. The proposed systemconsists of data acquisition, message analysis, sentiment evaluation, sentiment analysis and integration andresult visualization modules. The suggested approaches would help to improve the accuracy and expandthe application scope of sentiment analysis in social data.

Citation status

* References for papers published after 2022 are currently being built.