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

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3394171.3414813

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