Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm

Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom ac...

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Bibliographic Details
Published inFuture internet Vol. 12; no. 4; p. 75
Main Authors Yang, Wenzhong, Yuan, Tingting, Wang, Liejun
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2020
Subjects
Algorithms
Bagging
bagging algorithm
Classification
Classifiers
Dictionaries
Emotions
integration
Internet
long-term and short-term memory network
Machine learning
Microblogs
Personality
Sentiment analysis
sentiment classification
Support vector machines
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Summary:Integrated learning can be used to combine weak classifiers in order to improve the effect of emotional classification. Existing methods of emotional classification on micro-blogs seldom consider utilizing integrated learning. Personality can significantly influence user expressions but is seldom accounted for in emotional classification. In this study, a micro-blog emotion classification method is proposed based on a personality and bagging algorithm (PBAL). Introduce text personality analysis and use rule-based personality classification methods to divide five personality types. The micro-blog text is first classified using five personality basic emotion classifiers and a general emotion classifier. A long short-term memory language model is then used to train an emotion classifier for each set, which are then integrated together. Experimental results show that compared with traditional sentiment classifiers, PBAL has higher accuracy and recall. The F value has increased by 9%.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi12040075