关键词: 情感分类, 跨领域, 噪音, 边缘堆叠降噪自动编码器（mSDA）

Abstract: Neural network models are used to address domain adaptation. As a model of neural network, marginalized stacked denoising autoencoders (mSDA) can extract and encode more robust feature space. mSDA tends to learn a common and robust feature representation to solve the problem of domain adaptation by marginalizing corruption with noise to the source and target domain data. However, mSDA uses the same marginalized and denoising method to corrupt all features. But in fact, features have different effects on the classification. This paper tries to corrupt the different features with a variant noise, and proposes the approach named multi-marginalized denoising autoencoders (M-MDA) for domain adaptation. Firstly, a polarity index WLLRU (weighted log-likelihood ratio update) which is improved from weight likelihood ratio, is proposed to distinguish the shared features from specific features. Then, the shared features and specific features are corrupted with different noises, and the noise is computed according to the distance of features between the source and target domain. And then marginalized denoising autoencoders (MDA) is used to learn a more robust feature space with the corrupted data. Lastly, the new feature space is corrupted again to enhance the proportion of shared features. The experimental results show that the proposed method outperforms state-of-the-art methods in cross-domain sentiment classification.

Key words: sentiment classification, cross-domain, noise, marginalized stacked denoising autoencoders (mSDA)