Unsupervised Sentiment Analysis for Social Media Images
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 2378-2378.
Abstract
Recently text-based sentiment prediction has been extensively studied, while image-centric sentiment analysis receives much less attention. In this paper,we study the problem of understanding human sentiments from large-scale social media images,considering both visual content and contextual information,such as comments on the images, captions,etc. The challenge of this problem lies in the “semantic gap” between low-level visual features and higher-level image sentiments. Moreover, the lack of proper annotations/labels in the majority of social media images presents another challenge.To address these two challenges, we propose a novel Unsupervised SEntiment Analysis (USEA) framework for social media images. Our approach exploits relations among visual content and relevant contextual information to bridge the “semantic gap” in the prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.
@inproceedings{ijcai2015p336,
title = {Unsupervised Sentiment Analysis for Social Media Images},
author = {Wang, Yilin and Wang, Suhang and Tang, Jiliang and Liu, Huan and Li, Baoxin},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {2378--2378},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/336},
}
TY - CPAPER TI - Unsupervised Sentiment Analysis for Social Media Images AU - Wang, Yilin AU - Wang, Suhang AU - Tang, Jiliang AU - Liu, Huan AU - Li, Baoxin T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 2378 EP - 2378 UR - https://ijcai.org/proceedings/2015/336 ER -Copied
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