Combination of multi-view multi-source language classifiers for cross-lingual sentiment classification

Abstract

Cross-lingual sentiment classification aims to conduct sentiment classification in a target language using labeled sentiment data in a source language. Most existing research works rely on machine translation to directly project information from one language to another. But cross-lingual classifiers always cannot learn all characteristics of target language data by using only translated data from one language. In this paper, we propose a new learning model that uses labeled sentiment data from more than one language to compensate some of the limitations of resource translation. In this model, we first create different views of sentiment data via machine translation, then train individual classifiers in every view and finally combine the classifiers for final decision. We have applied this model to the sentiment classification datasets in three different languages using different combination methods. The results show that the combination methods improve the performances obtained separately by each individual classifier.