{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T15:27:55Z","timestamp":1759937275658,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,5,22]],"date-time":"2021-05-22T00:00:00Z","timestamp":1621641600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,22]]},"DOI":"10.1145\/3469968.3469970","type":"proceedings-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T22:21:24Z","timestamp":1633558884000},"page":"8-14","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning-Based COVID-19 Twitter Analysis"],"prefix":"10.1145","author":[{"given":"Yifei","family":"Song","sequence":"first","affiliation":[{"name":"Arcadia University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinran","family":"Wang","sequence":"additional","affiliation":[{"name":"Arcadia University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanxia","family":"Jia","sequence":"additional","affiliation":[{"name":"Arcadia University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,6]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"d19","author":"Dashboard","year":"2021","journal-title":"World Health Organization. Retrieved"},{"volume-title":"umap, and digraphs.\" arXiv preprint arXiv:2005.03082","year":"2020","author":"Ordun Catherine","key":"e_1_3_2_1_2_1"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.2196\/21978"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2196\/22624"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0239441"},{"issue":"3","key":"e_1_3_2_1_6_1","article-title":"Informational flow on Twitter\u2013Corona virus outbreak\u2013topic modelling approach","volume":"11","author":"Prabhakar Kaila Dr","year":"2020","journal-title":"International Journal of Advanced Research in Engineering and Technology (IJARET)"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Garcia Klaifer and Lilian Berton. \"Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.\" Applied Soft Computing 101:  107057.  Garcia Klaifer and Lilian Berton. \"Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.\" Applied Soft Computing 101: 107057.","DOI":"10.1016\/j.asoc.2020.107057"},{"volume-title":"Global Sentiment Analysis Of COVID-19 Tweets Over Time.\" arXiv preprint arXiv:2010.14234","year":"2020","author":"Mansoor Muvazima","key":"e_1_3_2_1_8_1"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/app9061123"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2012.00460.x"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Drias Habiba H. and Yassine Drias. \"Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery.\" medRxiv (2020).  Drias Habiba H. and Yassine Drias. \"Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery.\" medRxiv (2020).","DOI":"10.1101\/2020.05.08.20090464"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5256-5"},{"key":"e_1_3_2_1_13_1","first-page":"993","article-title":"\"Latent dirichlet allocation","author":"Blei David M","year":"2003","journal-title":"Journal of machine Learning research 3."},{"volume-title":"The 2010 annual conference of the North American","year":"2010","author":"Newman David","key":"e_1_3_2_1_14_1"},{"key":"e_1_3_2_1_15_1","unstructured":"pyLDAvis. PyPI. Retrieved January 2021 from https:\/\/pypi.org\/project\/pyLDAvis\/  pyLDAvis. PyPI. Retrieved January 2021 from https:\/\/pypi.org\/project\/pyLDAvis\/"},{"volume-title":"Affect in tweets.\" Proceedings of the 12th international workshop on semantic evaluation","year":"2018","author":"Mohammad Saif","key":"e_1_3_2_1_16_1"},{"volume-title":"\"Glove: Global vectors for word representation.\" Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)","year":"2014","author":"Pennington Jeffrey","key":"e_1_3_2_1_17_1"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"R\u00f6der Michael Andreas Both and Alexander Hinneburg. \"Exploring the space of topic coherence measures.\" Proceedings of the eighth ACM international conference on Web search and data mining. 2015.  R\u00f6der Michael Andreas Both and Alexander Hinneburg. \"Exploring the space of topic coherence measures.\" Proceedings of the eighth ACM international conference on Web search and data mining. 2015.","DOI":"10.1145\/2684822.2685324"}],"event":{"name":"ICBDC 2021: 2021 6th International Conference on Big Data and Computing","acronym":"ICBDC 2021","location":"Shenzhen China"},"container-title":["2021 6th International Conference on Big Data and Computing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3469968.3469970","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3469968.3469970","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:16Z","timestamp":1750195696000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3469968.3469970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,22]]},"references-count":18,"alternative-id":["10.1145\/3469968.3469970","10.1145\/3469968"],"URL":"https:\/\/doi.org\/10.1145\/3469968.3469970","relation":{},"subject":[],"published":{"date-parts":[[2021,5,22]]}}}