A wide range of approaches to sentiment analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., “great”, “terrible”). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., “great” is negative in the context “pain”) or the conceptual meaning associated with the words (e.g., “Bookworm” is negative when its associated semantic concept is “Worm”). The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider word’s semantics for sentiment analysis at both, entity and tweet levels, surpass non- semantic approaches in most datasets.