Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis
- Submitting institution
-
The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1459904
- Type
- E - Conference contribution
- DOI
-
10.1007/978-3-319-16354-3_49
- Title of conference / published proceedings
- Lecture Notes in Computer Science
- First page
- 447
- Volume
- 9022
- Issue
- -
- ISSN
- 0302-9743
- 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
-
2
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper goes beyond the usual word level representation for NLP by creating a new model for phrases and sentences that outperforms all other known models. The results from this ECIR paper (ECIR is a CORE A-rated conference) have found critical acclaim in the definitive survey paper of sentiment analysis in social media (Yue et al., 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -