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

College of Information Science and Engineering, Xinjiang University, Urumqi, Xinjiang 830046, China
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Future Internet 2020, 12(4), 75; https://doi.org/10.3390/fi12040075
Received: 6 March 2020 / Revised: 4 April 2020 / Accepted: 17 April 2020 / Published: 20 April 2020
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%. View Full-Text
Keywords: sentiment classification; personality; bagging algorithm; long-term and short-term memory network; integration sentiment classification; personality; bagging algorithm; long-term and short-term memory network; integration
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Yang, W.; Yuan, T.; Wang, L. Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm. Future Internet 2020, 12, 75.

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