Visual Sentiment Analysis with Noisy Labels by Reweighting Loss
Lin Wang, Xiangmin Xu, Kailing Guo, Bolun Cai · 2018
Visual sentiment analysis of online user generated content is important for many social media analysis tasks. However, label noise is common in sentiment analysis datasets, which deteriorate classification performance. To address this issue, we propose a novel visual sentiment analysis method based on loss reweighting to improve model robustness for label noise. First, a CNN is pre-trained with softmax loss on noisy labels datasets. Second, noise matrix is estimated by resorting and repositioning predicted probability, which is predicted by the pre-trained CNN. Third, converting noise estimation to the loss weight, the degeneration of sentiment classifiers performance caused by noisy labels can be compensated by re-training neural network with this reweighing loss. We conduct experiments on public sentiment datasets including Sentibank and Twitter datasets, and demonstrate that the proposed method outperforms state-of-the-art results.