124 related articles for article (PubMed ID: 38838308)
1. Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data.
Li W; Tang LM; Montayre J; Harris CB; West S; Antoniou M
J Med Internet Res; 2024 Jun; 26():e49450. PubMed ID: 38838308
[TBL] [Abstract][Full Text] [Related]
2. Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data.
Valdez D; Ten Thij M; Bathina K; Rutter LA; Bollen J
J Med Internet Res; 2020 Dec; 22(12):e21418. PubMed ID: 33284783
[TBL] [Abstract][Full Text] [Related]
3. Using Natural Language Processing to Explore Social Media Opinions on Food Security: Sentiment Analysis and Topic Modeling Study.
Molenaar A; Lukose D; Brennan L; Jenkins EL; McCaffrey TA
J Med Internet Res; 2024 Mar; 26():e47826. PubMed ID: 38512326
[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. Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic.
Xavier T; Lambert J
J Nurs Scholarsh; 2022 Sep; 54(5):613-622. PubMed ID: 35343050
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Public perceptions on Twitter of nurses during the COVID-19 pandemic.
Tokac U; Brysiewicz P; Chipps J
Contemp Nurse; 2022; 58(5-6):414-423. PubMed ID: 36370034
[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. 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]
12. 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]
13. 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]
14. Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study.
Pollack CC; Gilbert-Diamond D; Alford-Teaster JA; Onega T
J Med Internet Res; 2021 Jun; 23(6):e28648. PubMed ID: 34086591
[TBL] [Abstract][Full Text] [Related]
15. Vaping discussion in the COVID-19 pandemic: An observational study using Twitter data.
Lyu JC; Luli GK; Ling PM
PLoS One; 2021; 16(12):e0260290. PubMed ID: 34879077
[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. 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]
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. Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study.
Zhang C; Xu S; Li Z; Hu S
J Med Internet Res; 2021 Mar; 23(3):e26482. PubMed ID: 33617460
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]
