Abstract
Classifying images according to the sentiments expressed therein has a wide range of applications, such as sentiment-based search or recommendation. Most existing methods for image sentiment classification approach this problem by training general classifiers based on certain visual features, ignoring the discrepancies across domains. In this paper, we propose a novel co-training method with domain similarity (CoDS) for cross-domain image sentiment classification in social applications. The key idea underlying our approach is to use both the images and the corresponding textual comments when training classifiers, and to use the labeled data of one domain to make sentiment classification for the images of another domain through co-training. We compute image/text similarity between the source domain and the _target domain and set the weighting of the corresponding classifiers to improve performance. We perform extensive experiments on a real dataset collected from Flickr. The experimental results show that our proposed method significantly outperforms the baseline methods.
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Acknowledgment
This work was supported in part by the National Basic Research 973 Program of China under Grant No. 2015CB352502, the National Natural Science Foundation of China under Grant Nos. 61272092 and 61572289, the Natural Science Foundation of Shandong Province of China under Grant Nos. ZR2012FZ004 and ZR2015FM002, the Science and Technology Development Program of Shandong Province of China under Grant No. 2014GGE27178, and the NSERC Discovery Grants.
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Zhang, L., Chen, M., Yu, X., Liu, Y. (2016). CoDS: Co-training with Domain Similarity for Cross-Domain Image Sentiment Classification. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_39
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DOI: https://doi.org/10.1007/978-3-319-45814-4_39
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