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Therefore, it is significant to extract hotspots for monitoring Internet public opinion from online posts textual big data. However, the current hotspot extraction methods are focused on the users\u2019 features that are based on textual big data with spam and low-quality content. Meanwhile, these methods seldomly consider the time span of posts and the popularity of users. Accordingly, this article presents a hotspots information extraction hybrid solution of online posts\u2019 textual data. Firstly, a filtering strategy to obtain more high-quality textual data is designed. Secondly, the topic hot degree is presented by considering the average number of replies and the popularity of the participant. Thirdly, an improved co-word analysis technology is used to search the same topic posts and Bisecting k-means clustering algorithm using repliers\u2019 popularity and key posts are designed for studying and monitoring the hotspots of online posts in a valid big data environment. Finally, the proposed algorithms are verified in experiments by extracting the hotspots of online posts from the dataset. The results show that the data filtering strategy can help to obtain more valuable information and decrease the computing time. The results also demonstrate that the proposed solution can help to obtain hotspots comparing the traditional methods, and the hot degree can reflect the trend of the online post by comparing the traditional methods.<\/jats:p>","DOI":"10.1155\/2021\/6619712","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:55:41Z","timestamp":1618523741000},"page":"1-11","source":"Crossref","is-referenced-by-count":2,"title":["A Hotspot Information Extraction Hybrid Solution of Online Posts\u2019 Textual Data"],"prefix":"10.1155","volume":"2021","author":[{"given":"HuiRu","family":"Cao","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Guangzhou Institute of Technology, Guangzhou 510725, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7587-0543","authenticated-orcid":true,"given":"Xiaomin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China"}]},{"given":"Songyao","family":"Lian","sequence":"additional","affiliation":[{"name":"Nanfang College, Sun Yat-sen University, Guangzhou 510970, China"}]},{"given":"Choujun","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Computer, South China Normal University, Guangzhou 510631, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1089\/space.2015.0033"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1145\/3161603"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1108\/10662249710171814"},{"first-page":"1","article-title":"Predictive model for internet public opinion","author":"J. 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