원문정보
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
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