Abstract：Sentiment classification is a text classification task that extracts emotional tendencies from text. Ensemble learning combines several classifiers to achieve a better classification effect than component learners on the task of emotional classification. However, due to the different performance of component learners on data sets, the weights of component learners in the ensemble method were not well-distributed. Aiming at the weight optimization problem of component learners in ensemble learning, a classification method based on differential evolution to optimize the weights of component learners was proposed and applied to Chinese text sentiment classification. Using the classification accuracy as the fitness value, the weight combination of five component learners was optimized by differential evolution algorithm, and experiments were performed on the review corpus of the three fields. The experimental results showed that compared with the general ensemble method, the proposed classification method has better classification performance in sentiment classification.
杨梦月,卫伟,陆慧娟,卢海峰. 基于差分进化的中文情感分类集成算法研究[J]. 计量学报, 2020, 41(2): 225-230.
YANG Meng-yue,WEI Wei,LU Hui-juan,LU Hai-feng. Research on Ensemble Algorithm for Chinese Emotion Classification Based on Differential Evolution. Acta Metrologica Sinica, 2020, 41(2): 225-230.
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2020, Vol. 41 
