Earticle

초록

영어

With the increasing development of online collaborative platforms, there emerge massive subjective texts. However, due to the massive negative news about eroticism, violence, extremity and corruption, as well as the influences of agitators and provocateurs, it is quite likely that Internet users can be turned from conscious individuals into unconscious groups, which contributes to the accumulation of public negative sentiment. In this work, we focus on the identification of sentiment and especially negative sentiment. Specifically, we introduce sentiment layer to the basic LDA topic model to map the texts into a lower dimensional space of topics and sentiment. Besides, we also consider the sentiment dictionary based sentiment feature word extraction method. By feeding the feature words into Support Vector Machine (SVM) classifier, we get the sentiment tendency of texts. Our experiments prove the efficiency of proposed method.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Preliminaries
  3.1. LDA Model
  3.2. SVM Basics
 4. Proposed Method
  4.1. Sentiment Word Extraction
  4.2. Sentiment based LDA Model
 5. Experiment
 6. Conclusion
 References

키워드

저자

• Jun-Hui Zheng [ College of Information Engineering, Pingdingshan University, Pingdingshan Henan, China ]
• Gang-Li [ Modern Education Technology Center, Pingdingshan University, Pingdingshan Henan, China ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605