Unsupervised sentiment analysis for social media images

Yilin Wang, Suhang Wang, Jiliang Tang, Huan Liu, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Citations (Scopus)

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 prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.

Original languageEnglish (US)
Title of host publicationIJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence
EditorsMichael Wooldridge, Qiang Yang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages2378-2379
Number of pages2
ISBN (Electronic)9781577357384
StatePublished - Jan 1 2015
Event24th International Joint Conference on Artificial Intelligence, IJCAI 2015 - Buenos Aires, Argentina
Duration: Jul 25 2015Jul 31 2015

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2015-January
ISSN (Print)1045-0823

Other

Other24th International Joint Conference on Artificial Intelligence, IJCAI 2015
CountryArgentina
CityBuenos Aires
Period7/25/157/31/15

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Semantics
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Experiments

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Wang, Y., Wang, S., Tang, J., Liu, H., & Li, B. (2015). Unsupervised sentiment analysis for social media images. In M. Wooldridge, & Q. Yang (Eds.), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence (pp. 2378-2379). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2015-January). International Joint Conferences on Artificial Intelligence.
Wang, Yilin ; Wang, Suhang ; Tang, Jiliang ; Liu, Huan ; Li, Baoxin. / Unsupervised sentiment analysis for social media images. IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. editor / Michael Wooldridge ; Qiang Yang. International Joint Conferences on Artificial Intelligence, 2015. pp. 2378-2379 (IJCAI International Joint Conference on Artificial Intelligence).
@inproceedings{c5c7da329bac40c9b622ea885c8bcf14,
title = "Unsupervised sentiment analysis for social media images",
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 prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.",
author = "Yilin Wang and Suhang Wang and Jiliang Tang and Huan Liu and Baoxin Li",
year = "2015",
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language = "English (US)",
series = "IJCAI International Joint Conference on Artificial Intelligence",
publisher = "International Joint Conferences on Artificial Intelligence",
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Wang, Y, Wang, S, Tang, J, Liu, H & Li, B 2015, Unsupervised sentiment analysis for social media images. in M Wooldridge & Q Yang (eds), IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. IJCAI International Joint Conference on Artificial Intelligence, vol. 2015-January, International Joint Conferences on Artificial Intelligence, pp. 2378-2379, 24th International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, 7/25/15.

Unsupervised sentiment analysis for social media images. / Wang, Yilin; Wang, Suhang; Tang, Jiliang; Liu, Huan; Li, Baoxin.

IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. ed. / Michael Wooldridge; Qiang Yang. International Joint Conferences on Artificial Intelligence, 2015. p. 2378-2379 (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2015-January).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Wang, Yilin

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AU - Tang, Jiliang

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AU - Li, Baoxin

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N2 - 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 prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.

AB - 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 prediction of image sentiments. With experiments on two large-scale datasets, we show that the proposed method is effective in addressing the two challenges.

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M3 - Conference contribution

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Wang Y, Wang S, Tang J, Liu H, Li B. Unsupervised sentiment analysis for social media images. In Wooldridge M, Yang Q, editors, IJCAI 2015 - Proceedings of the 24th International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. 2015. p. 2378-2379. (IJCAI International Joint Conference on Artificial Intelligence).