Cross-lingual Twitter Polarity Detection via Projection across Word-Aligned Corpora

In this paper, we propose an unsupervised framework that leverages the sentiment resources and tools available in English language to automatically generate stand-alone polarity lexicons and classifiers for languages with scarce subjectivity resources and thus avoids the need for labor intensive manual annotation. Starting with a list of English sentiment-bearing words, we expand this lexicon using WordNet synsets. For each sentence pair in a given bilingual parallel corpus, the highprecision English polarity lexicon is applied to the English side then the output sentiment label is projected onto the target language side via statistically derived word alignments. The resulting lexicon is applied to a large pool of unlabeled tweets in the target language, in order to automatically label tweets as training data to train polarity classifier. Our experiments using Spanish and Portuguese as target ones have shown that the resulting classifiers help to improve polarity classification performance compared to lexicon-based classification for under-resourced languages in social media.