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In this paper, we describe how we created two state-of-the-art SVMclassifiers, one to detect the sentiment of messages such as tweets and SMS(message-level task) and one to detect the sentiment of a term within asubmissions stood first in both tasks on tweets, obtaining an F-score of 69.02in the message-level task and 88.93 in the term-level task. We implemented avariety of surface-form, semantic, and sentiment features. with sentiment-wordhashtags, and one from tweets with emoticons. In the message-level task, thelexicon-based features provided a gain of 5 F-score points over all others.Both of our systems can be replicated us available resources.

NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets