ArticlePloS one2018
Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
61 citing papers in PubMed, 114 citations in OpenAlex.
- Do Greener Urban Streets Provide Better Emotional Experiences? An Experimental Study on Chinese Tourists.International journal of environmental research and public health · 2022Trial
- Multidimensional analysis of social discourse on Twitter (X) around Venezuelan migration in Colombia.PloS one · 2026Article
- Impact of the COVID-19 Pandemic and the 2021 National Institute for Health and Care Excellence Guidelines on Public Perspectives Toward Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Thematic and Sentiment Analysis on Twitter (Rebranded as X).Journal of medical Internet research · 2025Article
- Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis.Journal of medical Internet research · 2025Article
- Unveiling Topics and Emotions in Arabic Tweets Surrounding the COVID-19 Pandemic: Topic Modeling and Sentiment Analysis Approach.JMIR infodemiology · 2025Article
- Cross-Cultural Sense-Making of Global Health Crises: A Text Mining Study of Public Opinions on Social Media Related to the COVID-19 Pandemic in Developed and Developing Economies.Journal of medical Internet research · 2025Article
- Using Twitter (X) to Mobilize Knowledge for First Contact Physiotherapists: Qualitative Study.Journal of medical Internet research · 2024Article
- Understanding the Long Haulers of COVID-19: Mixed Methods Analysis of YouTube Content.JMIR AI · 2024Article
- Twitter Analysis of Health Care Workers' Sentiment and Discourse Regarding Post-COVID-19 Condition in Children and Young People: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Postgraduate psychological stress detection from social media using BERT-Fused model.PloS one · 2024Article
- Towards a practical use of text mining approaches in electrodiagnostic data.Scientific reports · 2023Article
- Frontline Health Care Workers' Mental Health and Well-Being During the First Year of the COVID-19 Pandemic: Analysis of Interviews and Social Media Data.Journal of medical Internet research · 2023Article
- Enhancing Speech Emotion Recognition Using Dual Feature Extraction Encoders.Sensors (Basel, Switzerland) · 2023Article
- Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts.Journal of medical Internet research · 2023Observational
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.International journal of environmental research and public health · 2022Article
- Thinking Aloud or Screaming Inside: Exploratory Study of Sentiment Around Work.JMIR formative research · 2022Article
- Perceptions and Discussions of Snus on Twitter: Observational Study.JMIR medical informatics · 2022Article
- Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis.Journal of medical Internet research · 2022Article
- A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition.Sensors (Basel, Switzerland) · 2022Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we propose a sentiment-based approach to investigate the temporal and spatiotemporal effects on tourists' emotions when visiting a city's tourist destinations. Our approach consists of four steps: data collection and preprocessing from social media; visitor origin identification; visit sentiment identification; and temporal and spatiotemporal analysis. The temporal and spatiotemporal dimensions include day of the year, season of the year, day of the week, location sentiment progression, enjoyment measure, and multi-location sentiment progression. We apply this approach to the city of Chicago using over eight million tweets. Results show that seasonal weather, as well as special days and activities like concerts, impact tourists' emotions. In addition, our analysis suggests that tourists experience greater levels of enjoyment in places such as observatories rather than zoos. Finally, we find that local and international visitors tend to convey negative sentiment when visiting more than one attraction in a day whereas the opposite holds for out of state visitors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.