{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:18:37Z","timestamp":1743002317185,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030624590"},{"type":"electronic","value":"9783030624606"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62460-6_20","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"224-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Covid-19 Public Opinion Analysis Based on LDA Topic Modeling and Data Visualization"],"prefix":"10.1007","author":[{"given":"Li","family":"Chen","sequence":"first","affiliation":[]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ben","family":"Niu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"issue":"05","key":"20_CR1","first-page":"172","volume":"36","author":"GF Wang","year":"2016","unstructured":"Wang, G.F., Li, M.: Review and prospect of research on network public opinion in China. Mod. Intell. 36(05), 172\u2013176 (2016)","journal-title":"Mod. Intell."},{"key":"20_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TITS.2018.2868518","volume":"99","author":"Y Chen","year":"2018","unstructured":"Chen, Y., Lv, Y., Wang, X., et al.: Detecting traffic information from social media texts with deep learning approaches. IEEE Trans. Intell. Transp. Syst. 99, 1\u201310 (2018)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"5","key":"20_CR3","doi-asserted-by":"publisher","first-page":"733","DOI":"10.1080\/1369118X.2016.1203974","volume":"20","author":"R Medaglia","year":"2017","unstructured":"Medaglia, R., Yang, Y.: Online public deliberation in China: evolution of interaction patterns and network homophily in the Tianya discussion forum. Inf. Commun. Soc. 20(5), 733\u2013753 (2017)","journal-title":"Inf. Commun. Soc."},{"key":"20_CR4","doi-asserted-by":"publisher","unstructured":"Song, C., Guo, C., Hunt, K., et al.: An Analysis of Public Opinions Regarding Take-Away Food Safety: A 2015\u20132018 Case Study on Sina Weibo. Foods (Basel, Switzerland) (2020). https:\/\/doi.org\/10.3390\/foods9040511","DOI":"10.3390\/foods9040511"},{"key":"20_CR5","doi-asserted-by":"publisher","first-page":"4767","DOI":"10.3390\/ijerph16234767","volume":"16","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Chen, J., Liu, X.: Public perception of haze weather based on Weibo comments. Int. J. Environ. Res. Publ. Health 16, 4767 (2019). https:\/\/doi.org\/10.3390\/ijerph16234767","journal-title":"Int. J. Environ. Res. Publ. Health"},{"issue":"3","key":"20_CR6","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1108\/OIR-07-2017-0217","volume":"43","author":"S Wang","year":"2019","unstructured":"Wang, S., Song, Y.: Chinese online public opinions on the two-child policy. Online Inf. Rev. 43(3), 387\u2013403 (2019)","journal-title":"Online Inf. Rev."},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Jia, J., Lu, X., Yuan, Y., et al.: Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 1\u201311 (2020)","DOI":"10.1038\/s41586-020-2284-y"},{"issue":"2","key":"20_CR8","doi-asserted-by":"publisher","first-page":"e18700","DOI":"10.2196\/18700","volume":"6","author":"J Li","year":"2020","unstructured":"Li, J., Xu, Q., Cuomo, R., et al.: Data mining and content analysis of the Chinese social media platform Weibo during the early COVID-19 outbreak: retrospective observational infoveillance study. JMIR Publ. Health Surveill. 6(2), e18700 (2020). https:\/\/doi.org\/10.2196\/18700","journal-title":"JMIR Publ. Health Surveill."},{"key":"20_CR9","doi-asserted-by":"publisher","unstructured":"Qin, L., Sun, Q., Wang, Y., et al.: Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. Int. J. Environ. Res. Publ. Health 17(7) (2020). https:\/\/doi.org\/10.3390\/ijerph17072365","DOI":"10.3390\/ijerph17072365"},{"issue":"4\u20135","key":"20_CR10","first-page":"993","volume":"3","author":"D Blei","year":"2003","unstructured":"Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4\u20135), 993\u20131022 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR11","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.cities.2019.04.009","volume":"93","author":"F Capela","year":"2019","unstructured":"Capela, F., Ramirez-Marquez, J.: Detecting urban identity perception via newspaper topic modeling. Cities 93, 72\u201383 (2019)","journal-title":"Cities"},{"issue":"2","key":"20_CR12","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1080\/09537325.2019.1648789","volume":"32","author":"X Wang","year":"2020","unstructured":"Wang, X., Yang, X., Wang, X., et al.: Evaluating the competitiveness of enterprise\u2019s technology based on LDA topic model. Technol. Anal. Strateg. Manag. 32(2), 208\u2013222 (2020)","journal-title":"Technol. Anal. Strateg. Manag."},{"key":"20_CR13","volume-title":"Hands-on Data Analysis and Data Mining with Python","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Tan, L., Liu, M., et al.: Hands-on Data Analysis and Data Mining with Python, 2nd edn. China Machine Press, Beijing (2019)","edition":"2"},{"key":"20_CR14","unstructured":"Carson, S., Kenneth, S.: LDAvis: a method for visualizing and interpreting topics. In: Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, pp. 63\u201370. Association for Computational Linguistics, Maryland (2014)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62460-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,24]],"date-time":"2021-04-24T11:21:30Z","timestamp":1619263290000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-62460-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030624590","9783030624606"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62460-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}