Analysis of the effect of sentiment analysis on extracting adverse drug reactions from tweets and forum posts
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2349
- Type
- D - Journal article
- DOI
-
10.1016/j.jbi.2016.06.007
- Title of journal
- Journal of Biomedical Informatics
- Article number
- -
- First page
- 148
- Volume
- 62
- Issue
- -
- ISSN
- 1532-0464
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2016
- URL
-
https://e-space.mmu.ac.uk/620316/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
A - Data Science
- Citation count
- 55
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper links the areas of adverse drug reaction (ADR) detection and sentiment analysis and has an impact on the discovery of the true nature of medication’s impact upon users, shedding light on why some patients choose to stop taking medication against their medical professional’s wishes. This is especially pertinent in mental health where patient compliance can prevent relapses of mental health episodes. The work has inspired numerous studies on ADR detection and is referenced in a US patent on the use of cognitive frameworks to detect adverse events (US-10540438-B2) and on Wikipedia’s ‘Social Media Mining’ page.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -