Bridging the Language Gap: Learning Distributed Semantics for Cross-Lingual Sentiment Classification

Cross-lingual sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of data in a label-scare target language by exploiting labeled data from a label-rich language. The fundamental challenge of cross-lingual learning stems from a lack of overlap between the feature spaces of the source language data and that of the target language data. To address this challenge, previous work in the literature mainly relies on machine translation engines or bilingual lexicons to directly adapt labeled data from the source language to the target language. However, machine translation may change the sentiment polarity of the original data. In this paper, we propose a new model which uses stacked autoencoders to learn language-independent distributed representations for the source and target languages in an unsupervised fashion. Sentiment classifiers trained on the source language can be adapted to predict sentiment polarity of the target language with the language-independent distributed representations. We conduct extensive experiments on English-Chinese sentiment classification tasks of multiple data sets. Our experimental results demonstrate the efficacy of the proposed cross-lingual approach.