The main idea of existing CLSA researches is to first build up the connection between the source and target languages to overcome the language barrier, and then develop an appropriate knowledge transfer approach to leverage the annotated data from the source language to train a sentiment classification model in the target language, either supervised or semi-supervised.
these approaches exploit and convert the knowledge learned from the source language to automatically generate and expand the pseudo-training data for the target language.