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

• DOI: 10.3115/v1/s14-2077
• Publication Date: 2015-06-17T03:09:43Z
• Authors: Xiaodan Zhu; Svetlana Kiritchenko; Saif Mohammad
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology

This paper describes state-of-the-art statistical systems for automatic sentiment analysis 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 submissions obtained highest scores on the LiveJournal blog posts test set, sarcastic tweets test set, and the 2013 SMS test set. These systems build on our SemEval-2013 sentiment analysis systems (Mohammad et al., 2013) which ranked first in both the termand 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.