Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 51174182
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2015.2475761
- Title of journal
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- Article number
- -
- First page
- 398
- Volume
- 28
- Issue
- 2
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- 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
-
2
- Research group(s)
-
A - Computer Science
- Citation count
- 49
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Novel method to reduce annotation costs and improve algorithm scalability for sentiment analysis, and significantly impacts the retail industry.
Enabled funding for GBP400,000 (KTP12073) with VoiceIQ.
Pilot work of a visiting PhD project funded by the Chinese Scholarship Council (08/2016-08/2017, GBP11,400) that delivered a continuing work in IEEE TKDE (DOI: 10.1109/TKDE.2019.2913379, acceptance rate 14%), and of a PhD project in UoL with degree awarded in 2020.
Over 1,700 downloads and views since Sep 2015 (IEEE Xplore)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
