NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets

This paper describes state-of-the-art statis-tical systems for automatic sentiment anal-ysis of tweets. In a Semeval-2014 shared task (Task 9), our submissions obtained highest scores in the term-level sentiment classification subtask on both the 2013 and 2014 tweets test sets. In the message-level sentiment classification task, our submis-sions obtained highest scores on the Live-Journal blog posts test set, sarcastic tweets test set, and the 2013 SMS test set. These systems build on our SemEval-2013 senti-ment analysis systems (Mohammad et al., 2013) which ranked first in both the term-and message-level subtasks in 2013. Key improvements over the 2013 systems are in the handling of negation. We create separate tweet-specific sentiment lexicons for terms in affirmative contexts and in negated contexts. 1