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Weighted Co-Training for Cross-Domain Image Sentiment Classification

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Abstract

Image sentiment classification, which aims to predict the polarities of sentiments conveyed by the images, has gained a lot of attention. Most existing methods address this problem by training a general classifier with certain visual features, ignoring the discrepancies across domains. In this paper, we propose a novel weighted co-training method for cross-domain image sentiment classification, which iteratively enlarges the labeled set by introducing new high-confidence classified samples to reduce the gap between the two domains. We train two sentiment classifiers with both the images and the corresponding textual comments separately, and set the similarity between the source domain and the target domain as the weight of a classifier. We perform extensive experiments on a real Flickr dataset to evaluate the proposed method, and the empirical study reveals that the weighted co-training method significantly outperforms some baseline solutions.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, China

    Meng Chen, Lin-Lin Zhang, Xiaohui Yu & Yang Liu

  2. School of Information Technology, York University, Toronto, M3J 1P3, Canada

    Meng Chen & Xiaohui Yu

Authors
  1. Meng Chen
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  2. Lin-Lin Zhang
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  3. Xiaohui Yu
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  4. Yang Liu
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Correspondence to Yang Liu.

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Chen, M., Zhang, LL., Yu, X. et al. Weighted Co-Training for Cross-Domain Image Sentiment Classification. J. Comput. Sci. Technol. 32, 714–725 (2017). https://doi.org/10.1007/s11390-017-1753-8

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