The other way is to train a suitable deep learning model (Collobert et al., 2011;Mikolov et al., 2013a) on a raw corpus in that language and then use the obtained embeddings of these in-language words as input to the sentiment classification model.
learning context-rich word embeddings in any language requires large datasets, generally of the order of billions of words, thereby eating up a lot of time as well as system resources.