Learning user and product distributed representations using a sequence model for sentiment analysis
- Submitting institution
-
Aston University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 23024823
- Type
- D - Journal article
- DOI
-
10.1109/MCI.2016.2572539
- Title of journal
- IEEE Computational Intelligence Magazine
- Article number
- -
- First page
- 34
- Volume
- 11
- Issue
- 3
- ISSN
- 1556-603X
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- 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
-
4
- Research group(s)
-
A - Aston Institute of Urban Technology and the Environment (ASTUTE)
- Citation count
- 42
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper proposed a novel neural model combining both user and product distributed representations which achieved the state-of-the-art performance on three large-scale product review datasets from the IMDB and Yelp. It impacted work on review score prediction (Gupta, Microsoft), adversarial training in affective computing (Schuller, Imperial College London), recommendation (Lee, Hong Kong University of Science and Technology). Monitoring real-time review stream (Federici, University of Amsterdam) and sentiment prediction (Sun, Nanyang Technological University)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
