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The value of subjective content available in Social Media has boosted the importance of Sentiment Analysis on this kind of scenario. However, performing Sentiment Analysis on Social Media is a challenging task, since the huge volume of short textual posts and high dynamicity inherent to it pose strict requirements of efficiency and scalability. Despite all efforts, the literature still lacks proposals that address both requirements. In this sense, we propose LEGi, a corpus-based method for consolidating context-aware sentiment lexicons. It is based on a semi-supervised strategy for propagation of lexicon-semantic classes on a transition graph of terms. Empirical analyses on two distinct domains, derived from Twitter, demonstrate that LEGi outperformed four wellestablished methods for lexicon consolidation. Further, we found that LEGi's lexicons may improve the quality of the sentiment analysis performed by a traditional method in the literature. Thus, our results point out LEGi as a promising method for consolidating lexicons in high demanding scenarios, such as Social Media.

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