Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2020
Abstract
Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.
Keywords
Review rating prediction, Deep neural networks, Matrix factorization, Sentiment analysis, User-product interaction
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
International Journal of Machine Learning and Cybernetics
Volume
12
Issue
2
First Page
477
Last Page
488
ISSN
1868-808X
Identifier
10.1007/s13042-020-01181-9
Publisher
Springer
Embargo Period
7-31-2021
Citation
FENG, Shi; SONG, Kaisong; WANG, Daling; GAO, Wei; and ZHANG, Yifei.
InterSentiment: Combining deep neural models on interaction and sentiment for review rating prediction. (2020). International Journal of Machine Learning and Cybernetics. 12, (2), 477-488.
Available at: https://ink.library.smu.edu.sg/sis_research/5646
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s13042-020-01181-9
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons