Developing a standardized protocol for computational sentiment analysis research using health-related social media data Article Swipe
Lu He
,
Tingjue Yin
,
Zhaoxian Hu
,
Yunan Chen
,
David A. Hanauer
,
Kai Zheng
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa298
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1093/jamia/ocaa298
Objective Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies. Materials and Methods We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019. Results Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted. Conclusions A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.
Related Topics
Concepts
Comparability
Protocol (science)
Computer science
Data science
Social media
Sentiment analysis
Research design
Inclusion (mineral)
Relevance (law)
Path analysis (statistics)
Systematic review
Protocol design
Management science
MEDLINE
Psychology
Medicine
Alternative medicine
Social science
Artificial intelligence
World Wide Web
Social psychology
Sociology
Machine learning
Combinatorics
Economics
Political science
Mathematics
Operating system
Communications protocol
Law
Pathology
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/jamia/ocaa298
- OA Status
- green
- Cited By
- 17
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3114713384
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3114713384Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jamia/ocaa298Digital Object Identifier
- Title
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Developing a standardized protocol for computational sentiment analysis research using health-related social media dataWork title
- Type
-
reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-05Full publication date if available
- Authors
-
Lu He, Tingjue Yin, Zhaoxian Hu, Yunan Chen, David A. Hanauer, Kai ZhengList of authors in order
- Landing page
-
https://doi.org/10.1093/jamia/ocaa298Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/8200276Direct OA link when available
- Concepts
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Comparability, Protocol (science), Computer science, Data science, Social media, Sentiment analysis, Research design, Inclusion (mineral), Relevance (law), Path analysis (statistics), Systematic review, Protocol design, Management science, MEDLINE, Psychology, Medicine, Alternative medicine, Social science, Artificial intelligence, World Wide Web, Social psychology, Sociology, Machine learning, Combinatorics, Economics, Political science, Mathematics, Operating system, Communications protocol, Law, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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17Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 3, 2023: 5, 2022: 4, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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39Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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