Cross-ratio uninorms as an effective aggregation mechanism in sentiment analysis
- Submitting institution
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The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 17
- Type
- D - Journal article
- DOI
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10.1016/j.knosys.2017.02.028
- Title of journal
- Knowledge-Based Systems
- Article number
- -
- First page
- 16
- Volume
- 124
- Issue
- -
- ISSN
- 0950-7051
- Open access status
- Technical exception
- Month of publication
- February
- Year of publication
- 2017
- 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
- 21
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This output builds upon previous publications of the authors (https://doi.org/10.1016/j.knosys.2016.05.040, https://doi.org/10.12700/aph.12.3.2015.3.6.), and shows that a combination of non lexicon-based classification methods with specific uninorm operators improves the classification performance of lexicon-based methods for sentiment analysis, enabling the offering of an alternative solution to the sentient analysis classification problem. The output's extended version of cross-ratio uninorm is used in Basiri and Kabiri ‘s work reported in their Knowledge Based Systems paper (section IIIB in DOI: 10.1109/ICWR.2018.8387244) for sentence level scoring, on account of this output’s success showing that the aggregation method outperforms the arithmetic mean aggregation method (section II, same reference).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -