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

Wenzhong Yang, Tingting Yuan and Liejun Wang
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Wenzhong Yang: College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
Tingting Yuan: College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
Liejun Wang: College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China

Future Internet, 2020, vol. 12, issue 4, 1-14

Abstract: 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%.

Keywords: sentiment classification; personality; bagging algorithm; long-term and short-term memory network; integration (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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