{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:56:28Z","timestamp":1765356988635,"version":"3.37.3"},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3111833","type":"journal-article","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T20:20:52Z","timestamp":1631305252000},"page":"126684-126697","source":"Crossref","is-referenced-by-count":16,"title":["Senti-COVID19: An Interactive Visual Analytics System for Detecting Public Sentiment and Insights Regarding COVID-19 From Social Media"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9388-7282","authenticated-orcid":false,"given":"Xuemin","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3078-9634","authenticated-orcid":false,"given":"Martha Dais","family":"Ferreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2316-760X","authenticated-orcid":false,"given":"Fernando V.","family":"Paulovich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref10","first-page":"450","article-title":"Modeling public mood and emotion","author":"bollen","year":"2011","journal-title":"Proc ICSWSM"},{"key":"ref11","first-page":"66","article-title":"Not all moods are created equal","author":"choudhury","year":"2012","journal-title":"Proc ICSWSM"},{"journal-title":"Twitter Sentiment Analysis on Coronavirus using Textblob","year":"2020","author":"kaur","key":"ref12"},{"key":"ref13","first-page":"704","article-title":"Sentimental analysis of Twitter comments on Covid-19","author":"raheja","year":"2021","journal-title":"Proc 11th Int Conf Cloud Comput Data Sci Eng (Confluence)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3001216"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3012595"},{"key":"ref16","article-title":"Mining Twitter data on COVID-19 for sentiment analysis and frequent patterns discovery","author":"drias","year":"2020","journal-title":"medRxiv"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1978942.1978975"},{"journal-title":"Coronavirus (COVID-19) Tweets Dataset","year":"2020","author":"lamsal","key":"ref18"},{"journal-title":"Coronavirus (COVID-19) Geo-Tagged Tweets Dataset","year":"2020","author":"lamsal","key":"ref19"},{"journal-title":"Twitter","year":"2021","key":"ref28"},{"key":"ref4","first-page":"26","article-title":"Emotions evoked by common words and phrases: Using mechanical Turk to create an emotion lexicon","author":"mohammad","year":"2010","journal-title":"Proc Workshop Comput Approaches Anal Gener Emotion Text (NAACL HLT)"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-76941-7_80"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.3390\/info11060314"},{"journal-title":"Analyzing COVID19 Tweets Using Health Behaviours Theories and Classification Models","year":"2021","author":"graham-kalio","key":"ref6"},{"journal-title":"Twitter","year":"2021","key":"ref29"},{"key":"ref5","article-title":"Twitter sentiment analysis during COVID19 outbreak","author":"dubey","year":"2020","journal-title":"Available at SSRN 3572023"},{"journal-title":"Simplifying sentiment analysis using vader in python (on social media text)","year":"2018","author":"pandey","key":"ref8"},{"journal-title":"Live Twitter Sentiment|Analyzing COVID-19 Tweets","year":"2020","author":"lamsal","key":"ref7"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/VAST.2014.7042496"},{"journal-title":"Natural Language Processing With Python","year":"2019","author":"bird","key":"ref9"},{"journal-title":"The &#x2018;Yellow Light Rule&#x2019; Yields to Public Opinion|China Digital Times (CDT)","year":"2013","author":"rudolph","key":"ref1"},{"journal-title":"Developer Agreement and Policy","year":"2020","key":"ref20"},{"journal-title":"Sentiment Analysis","year":"2020","key":"ref22"},{"journal-title":"Twarc","year":"2021","author":"summers","key":"ref21"},{"journal-title":"Python|Sentiment Analysis Using Vader","year":"2019","key":"ref24"},{"journal-title":"Fine-Grained Sentiment Analysis in Python (Part 1)","year":"2019","author":"rao","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-76941-7_63"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.09.013"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09535159.pdf?arnumber=9535159","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:33Z","timestamp":1639770933000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9535159\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3111833","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}