Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2020
Abstract
We revisit our contributions on visual sentiment analysis for online review images published at ACM Multimedia 2017, where we develop item-oriented and user-oriented convolutional neural networks that better capture the interaction of image features with specific expressions of users or items. In this work, we outline the experimental claims as well as describe the procedures to reproduce the results therein. In addition, we provide artifacts including data sets and code to replicate the experiments.
Keywords
visual sentiment analysis, convolutional neural networks
Discipline
Databases and Information Systems | Data Science
Research Areas
Data Science and Engineering
Publication
MM '20: Proceedings of the 28th ACM International Conference on Multimedia, Seattle, October 12-16
First Page
4444
Last Page
4447
ISBN
9781450379885
Identifier
10.1145/3394171.3414813
Publisher
ACM
City or Country
New York
Embargo Period
5-20-2021
Citation
TRUONG, Quoc Tuan; LAUW, Hady W.; AUMULLER, Martin; and NITTA, Naoko.
Visual sentiment analysis for review images with item-oriented and user-oriented CNN: Reproducibility companion paper. (2020). MM '20: Proceedings of the 28th ACM International Conference on Multimedia, Seattle, October 12-16. 4444-4447.
Available at: https://ink.library.smu.edu.sg/sis_research/5955
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/10.1145/3394171.3414813