1046 related articles for article (PubMed ID: 35418205)
1. Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.
Ng JY; Abdelkader W; Lokker C
BMC Complement Med Ther; 2022 Apr; 22(1):105. PubMed ID: 35418205
[TBL] [Abstract][Full Text] [Related]
2. Public Health Surveillance of Behavioral Cancer Risk Factors During the COVID-19 Pandemic: Sentiment and Emotion Analysis of Twitter Data.
Christodoulakis N; Abdelkader W; Lokker C; Cotterchio M; Griffith LE; Vanderloo LM; Anderson LN
JMIR Form Res; 2023 Nov; 7():e46874. PubMed ID: 37917123
[TBL] [Abstract][Full Text] [Related]
3. Tracking Public Attitudes Toward COVID-19 Vaccination on Tweets in Canada: Using Aspect-Based Sentiment Analysis.
Jang H; Rempel E; Roe I; Adu P; Carenini G; Janjua NZ
J Med Internet Res; 2022 Mar; 24(3):e35016. PubMed ID: 35275835
[TBL] [Abstract][Full Text] [Related]
4. COVID-19 Vaccine-Related Discussion on Twitter: Topic Modeling and Sentiment Analysis.
Lyu JC; Han EL; Luli GK
J Med Internet Res; 2021 Jun; 23(6):e24435. PubMed ID: 34115608
[TBL] [Abstract][Full Text] [Related]
5. Emotions and Topics Expressed on Twitter During the COVID-19 Pandemic in the United Kingdom: Comparative Geolocation and Text Mining Analysis.
Alhuzali H; Zhang T; Ananiadou S
J Med Internet Res; 2022 Oct; 24(10):e40323. PubMed ID: 36150046
[TBL] [Abstract][Full Text] [Related]
6. Verification in the Early Stages of the COVID-19 Pandemic: Sentiment Analysis of Japanese Twitter Users.
Ueda R; Han F; Zhang H; Aoki T; Ogasawara K
JMIR Infodemiology; 2024 Feb; 4():e37881. PubMed ID: 38127840
[TBL] [Abstract][Full Text] [Related]
7. Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.
Hung M; Lauren E; Hon ES; Birmingham WC; Xu J; Su S; Hon SD; Park J; Dang P; Lipsky MS
J Med Internet Res; 2020 Aug; 22(8):e22590. PubMed ID: 32750001
[TBL] [Abstract][Full Text] [Related]
8. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study.
Chandrasekaran R; Mehta V; Valkunde T; Moustakas E
J Med Internet Res; 2020 Oct; 22(10):e22624. PubMed ID: 33006937
[TBL] [Abstract][Full Text] [Related]
9. Tweet Topics and Sentiments Relating to COVID-19 Vaccination Among Australian Twitter Users: Machine Learning Analysis.
Kwok SWH; Vadde SK; Wang G
J Med Internet Res; 2021 May; 23(5):e26953. PubMed ID: 33886492
[TBL] [Abstract][Full Text] [Related]
10. Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis.
Ainley E; Witwicki C; Tallett A; Graham C
J Med Internet Res; 2021 Oct; 23(10):e31101. PubMed ID: 34469327
[TBL] [Abstract][Full Text] [Related]
11. Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach.
Xue J; Chen J; Hu R; Chen C; Zheng C; Su Y; Zhu T
J Med Internet Res; 2020 Nov; 22(11):e20550. PubMed ID: 33119535
[TBL] [Abstract][Full Text] [Related]
12. Topics and Sentiments of Public Concerns Regarding COVID-19 Vaccines: Social Media Trend Analysis.
Monselise M; Chang CH; Ferreira G; Yang R; Yang CC
J Med Internet Res; 2021 Oct; 23(10):e30765. PubMed ID: 34581682
[TBL] [Abstract][Full Text] [Related]
13. Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis.
Roe C; Lowe M; Williams B; Miller C
Int J Environ Res Public Health; 2021 Dec; 18(24):. PubMed ID: 34948638
[TBL] [Abstract][Full Text] [Related]
14. Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.
Bari A; Heymann M; Cohen RJ; Zhao R; Szabo L; Apas Vasandani S; Khubchandani A; DiLorenzo M; Coffee M
Clin Infect Dis; 2022 May; 74(Suppl_3):e4-e9. PubMed ID: 35568473
[TBL] [Abstract][Full Text] [Related]
15. Emergency Physician Twitter Use in the COVID-19 Pandemic as a Potential Predictor of Impending Surge: Retrospective Observational Study.
Margus C; Brown N; Hertelendy AJ; Safferman MR; Hart A; Ciottone GR
J Med Internet Res; 2021 Jul; 23(7):e28615. PubMed ID: 34081612
[TBL] [Abstract][Full Text] [Related]
16. Surveilling COVID-19 Emotional Contagion on Twitter by Sentiment Analysis.
Crocamo C; Viviani M; Famiglini L; Bartoli F; Pasi G; Carrà G
Eur Psychiatry; 2021 Feb; 64(1):e17. PubMed ID: 33531097
[TBL] [Abstract][Full Text] [Related]
17. Twitter discussions on breastfeeding during the COVID-19 pandemic.
Jagarapu J; Diaz MI; Lehmann CU; Medford RJ
Int Breastfeed J; 2023 Nov; 18(1):56. PubMed ID: 37925408
[TBL] [Abstract][Full Text] [Related]
18. Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.
Lau N; Zhao X; O'Daffer A; Weissman H; Barton K
JMIR Cancer; 2024 May; 10():e52061. PubMed ID: 38713506
[TBL] [Abstract][Full Text] [Related]
19. Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers.
Yu H; Yang CC; Yu P; Liu K
PLoS One; 2022; 17(3):e0264794. PubMed ID: 35259181
[TBL] [Abstract][Full Text] [Related]
20. Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre- and Peri-COVID-19 Pandemic Retrospective Study.
Maghsoudi A; Nowakowski S; Agrawal R; Sharafkhaneh A; Kunik ME; Naik AD; Xu H; Razjouyan J
J Med Internet Res; 2022 Dec; 24(12):e41517. PubMed ID: 36417585
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
