In this paper, we describe how we created two state-of-the-art SVM classifiers, 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 a message (term-level task). Among submissions from 44 teams in a competition, our submissions stood first in both tasks on tweets, obtaining an F-score of 69.02 in the message-level task and 88.93 in the term-level task. We implemented a variety of surface-form, semantic, and sentiment features. We also generated two large word-sentiment association lexicons, one from tweets with sentiment-word hashtags, and one from tweets with emoticons. In the message-level task, the lexicon-based features provided a gain of 5 F-score points over all others. Both of our systems can be replicated using freely available resources. 1
On Twitter, REF develop a state-of-the-art Twitter sentiment classifier in SemEval 2013, using a variety of sentiment lexicons and hand-crafted features.
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
*SEM
Computer Science