Language-independent sentiment classification using three common words

Many methods for cross-lingual processing tasks are resource-dependent, which will not work without machine translation system or bilingual lexicon. In this paper, we propose a novel approach for multilingual sentiment classification just by few seed words. For a given language, the proposed approach learns a sentiment classifier from the initial seed words instead of any labeled data. We employ our method both in supervised learning and unsupervised learning. Experimental results demonstrate that our method relies less on external resource but performs as well as or better than the baseline.