Earticle

초록

영어

Because the accuracy of traditional sentiment orientation identification algorithm is not high under Q&A community, this paper proposes a new method based on two-level conditional random field improved by particle swarm optimization algorithm for emotion tendency recognition under Q&A community. The proposed method adopts particle swarm optimization algorithm to train two-level conditional random field model, and applies the trained conditional random field model to recognize emotion orientation of question-answer pairs in Q&A community. Experiments were performed on Yahoo! Answers data set and results show that the proposed two-level conditions random field improved by particle swarm optimization algorithm has a higher precision rate, recall rate and F1 value at the micro average and macro average aspects compared with Hidden Markov Model, Max-Entropy Markov Model, Support Vector Machine and traditional condition random domain model, which prove the proposed two-level conditions random field improved by particle swarm optimization algorithm is a more effective method to recognize emotion orientation of question-answer pairs in Q&A community.

목차

Abstract
 1. Introduction
 2. Related Work
 3. The Proposed Two-level CRF Model Improved by Particle Swarm Optimization Algorithm
  3.1. Particle Swarm Optimization Algorithm
  3.2. Introduction to the Proposed Two-level CRF Model
  3.3. Two-level CRF Model Improved by Particle Swarm Optimization Algorithm
 4. Experimental Analyses
  4.1. Experimental Data Set
  4.2. Experimental Tools and Software
  4.3. Experimental Evaluation Indexes
  4.4. Experiment Results Analysis
 5. Conclusion and Future Work
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Wang Caiyin [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China, School of Mechanical Electric and Engineering, Suzhou University, Suzhou 234000, Anhui, China ]
• Cui Lin [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China, College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China ]
• Li Hong [ Intelligent Information Processing Laboratory, Suzhou University, Suzhou 234000, Anhui, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605