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%.
Highlights
With the rapid development and maturity of Internet technologies, many online social platforms have gradually become the primary medium for people to obtain information and communicate with each other
personality and bagging algorithm (PBAL)-high conscientiousness datasets (HC) indicates that the HC personality is not considered, PBAL-high agreeableness datasets (HA) indicates that HA personality is not considered, PBAL-low agreeableness datasets (LA) indicates that the LA personality is not considered, PBAL-high extroversion datasets (HE) indicates that HE personality is not considered, and PBAL-low extroversion dataset (LE) indicates that LE personality is not considered
Significant similarities in user expressions have been shown for users with the same personality type
Summary
With the rapid development and maturity of Internet technologies, many online social platforms have gradually become the primary medium for people to obtain information and communicate with each other. Emerging social platforms, such as Weibo and WeChat, allow users to interact with information quickly and . While expressing opinions, spreading thoughts, and expressing personal emotions, users generate a large amount of information with personal subjective emotional characteristics This information contains emotional characteristics of different tendencies. With developments in psychological research, people with the same personality have been found to exhibit similarities in writing and expressions This feature is the basis for introducing personality into sentiment analysis.
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