Cross-Lingual Sentiment Classification Based on Denoising Autoencoder

Sentiment classification system relies on high-quality emotional resources. However, these resources are imbalanced in different languages. The way of how to leverage rich labeled data of one language (source language) for the sentiment classification of resource-poor language (target language), namely cross-lingual sentiment classification (CLSC), becomes a focus topic. This paper utilizes rich English resources for Chinese sentiment classification. To eliminate the language gap between English and Chinese, this paper proposes a combination CLSC approach based on denoising autoencoder. First, two classifiers based on denoising autoencoder are learned respectively in English and Chinese views by using English corpus and English-to-Chinese corpus. Second, we classify Chinese test data and Chinese-to-English test data with the two classifiers trained in the two views. Last, the final sentiment classification results are obtained by the combination of the two results in two views. Experiments are carried out on NLP&CC 2013 CLSC dataset including book, DVD and music categories. The results show that our approach achieves the accuracy of 80.02%, which outperforms the current state-of-the-art systems.