Contextual semantics for sentiment analysis of Twitter
- Submitting institution
-
The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1458775
- Type
- D - Journal article
- DOI
-
10.1016/j.ipm.2015.01.005
- Title of journal
- Information Processing and Management
- Article number
- -
- First page
- 5
- Volume
- 52
- Issue
- 1
- ISSN
- 0306-4573
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- 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
-
3
- Research group(s)
-
-
- Citation count
- 162
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper is first to introduce a dynamic context-dependent sentiment model, validated over three large datasets, and outperforming popular baselines. Published in IPM, a high impact journal in computing and information science. Cited for its model’s ability to change sentiment of words to fit a given topic and context (Giatsoglou et al., 2017, Pandey et al., 2017, Naseem et al., 2020) and is used to benchmark other models (Jianqiang et al., 2018). Received an honourable mention for best paper in IPM in 2015, and the PhD thesis which created this work won the Semantic Web Distinguished Dissertation Award in 2016.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
