Aggregated Topic Models for Increasing Social Media Topic Coherence
- Submitting institution
-
University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 76485075
- Type
- D - Journal article
- DOI
-
10.1007/s10489-019-01438-z
- Title of journal
- Applied Intelligence
- Article number
- -
- First page
- 138
- Volume
- 50
- Issue
- 1
- ISSN
- 0924-669X
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- 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
-
2
- Research group(s)
-
B - Artificial Intelligence Research Centre
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <17>This work was a follow-up study from a Knowledge Transfer Partnership (KTP) with Repknight Ltd. (Certificate No. KTP009125, 03/2013-02/2015) on sentiment mining, ranked as ‘outstanding’, the highest grade of KTP completion. The first author (Stuart Blair), a KTP fellow, has since been employed by Adobeboard Ltd. Results from this work have contributed to the development of Adobeboard’s product - Emotics Platform (Chris Johnston, https://adoreboard.com) that connects the emotional intensity discovered in unstructured text with the topics that matter most.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -