Emotional Sentence Annotation Helps Predict Fiction Genre
- Submitting institution
-
The University of Essex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1099
- Type
- D - Journal article
- DOI
-
10.1371/journal.pone.0141922
- Title of journal
- PLoS One
- Article number
- e0141922
- First page
- e0141922
- Volume
- 10
- Issue
- 11
- ISSN
- 1932-6203
- Open access status
- Out of scope for open access requirements
- Month of publication
- November
- Year of publication
- 2015
- URL
-
-
- 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
- No
- Number of additional authors
-
1
- Research group(s)
-
A - Artificial Intelligence (AI)
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper was the first demonstration of a simple application of data science to differentiate between different genres of literature (e.g. science fiction vs horror) via detecting emotional content. Since publication the tools have opened new frontiers in digital humanities, bringing data science methods to a wider audience. In addition to research citations the paper received coverage by Science (the magazine/journal - https://www.sciencemag.org/news/2015/11/computers-can-get-emotional-feel-fiction) and has also been discussed by various blogs and review papers (e.g. https://arxiv.org/pdf/1808.03137.pdf). It was a seminal work that helped establish usage of data science to link fiction genre and emotions.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -