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| Discovering Topics of Online Health Community with Q-LDA Model |
Lei Yang,Zirun Wang,Guisheng Hou( ) |
| College of Economics and Management, Shandong University of Science and Technology, Qingdao 266510, China |
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Abstract [Objective] This paper builds a Q-LDA model to identify topics of online health community, aiming to improve the quality of information generated by the LDA model, as well as its theme representation ability. [Methods] Firstly, we evaluated and weighted the online health information. Then, we constructed a Q-LDA topic mining model based on the LDA model. Finally, we examined the proposed model with real world data. [Results] The Q-LDA model yielded better results than the traditional LDA model. The efficiency of extracting topics was improved by 16%. [Limitations] We only examined the proposed model with textual data from online discussion boards on one disease. [Conclusions] Adding quality of health information to data mining could help us meet the needs of users.
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Received: 18 March 2019
Published: 18 December 2019
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Corresponding Authors:
Guisheng Hou
E-mail: houguisheng001@163.com
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