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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 29 / No.1: The Twenty-Ninth Conference on Artificial Intelligence

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

March 8, 2023

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Authors

Quanzeng You

University of Rochester


Jiebo Luo

University of Rochester


Hailin Jin

Adobe Research


Jianchao Yang

Adobe Research


DOI:

10.1609/aaai.v29i1.9179


Abstract:

Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.

Topics: AAAI

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HOW TO CITE:

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks Proceedings of the AAAI Conference on Artificial Intelligence, 29 (2015) .

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks AAAI 2015, .

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang (2015). Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 29, .

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 29 2015 p..

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. 2015. Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. "Proceedings of the AAAI Conference on Artificial Intelligence, 29". .

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. (2015) "Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks", Proceedings of the AAAI Conference on Artificial Intelligence, 29, p.

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang, "Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks", AAAI, p., 2015.

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. "Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 29, 2015, p..

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. "Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 29, (2015): .

Quanzeng You|| Jiebo Luo|| Hailin Jin|| Jianchao Yang. Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks. AAAI[Internet]. 2015[cited 2023]; .


ISSN: 2374-3468


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