Earticle

초록

영어

In today’s world, the web has dramatically changed the way that people express their opinions. People use the internet to express their opinion, attitude, feeling and emotion about films, goods, news etc. It is challenging to automatically classify mass subjectivity comments into different sentiment orientation categories (e.g. positive/negative). Furthermore, the ambiguity and randomness, which are existed in natural language, lead to lower classification accuracy in text sentiment classification. In this paper, we propose a novel chinese text sentiment classification algorithm based on mixed cloud vector model clustering and kernel fisher discriminant. In this algorithm, we firstly analysis the role of cloud model theory in conversion between qualitative concept and quantitative values, and explore a mixed feature cloud model (MFCM) based on cloud model to represent a single document. In MFCM, both effect of different part-of- speech features and ambiguity of sentiment tendency are considered. And then, documents are clustered according to their similarity between MFCM. Finally, kernel fisher discriminant (KFD) is adopted as the classifier to judge views. The experimental results demonstrate that our proposed method outperforms traditional approaches.

목차

Abstract
 1. Introduction
 2. Cloud Model Theory
 3. Sentiment Classification Based On CVMC and KFD
  3.1. Mixed-Feature Selection
  3.2. Cloud Vector Model Clustering (CVMC)
  3.3. Classifier Based On KFD
 4. Experiments
 5. Conclusions
 References

키워드

저자

• Yujuan Xing [ School of digital media, Lanzhou University of Arts and Science, Lanzhou, 730000, China ]
• Ping Tan [ School of digital media, Lanzhou University of Arts and Science, Lanzhou, 730000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605