Open AccessResearch on Emotion Classification based on Complex Network and Ensemble Learning
Author(s) -
Qingqing Cao,
Xiangyang Chen,
Yuanzhe Lai,
Chenzhou Deng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/3/032045
Subject(s) - computer science , feature selection , artificial intelligence , word (group theory) , selection (genetic algorithm) , feature (linguistics) , semantic feature , word lists by frequency , feature extraction , pattern recognition (psychology) , natural language processing , machine learning , mathematics , linguistics , philosophy , geometry , sentence
Because the traditional feature extraction is based on the statistical information such as document frequency and word frequency, the selection of feature words is ignored, and the semantic correlation between words in the text is ignored. The feature selection method based on complex network takes into account the semantic association between words, but does not take into account the statistical information such as word frequency. The above methods are not satisfactory for the selection of feature words, which affects the effect of text classification. Therefore, this paper combines the two, proposes a new method for feature selection, and in order to solve the problem of low accuracy rate of single classification algorithm, USES integrated learning [1] to strengthen the classification algorithm. The results show that this method is feasible and achieves good classification effect.