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

Xiaodan Zhu, Svetlana Kiritchenko, Saif Mohammad
2014 Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)  
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 Live-Journal blog posts test set, sarcastic tweets test set, and the 2013 SMS test set. These systems build on our
more » ... 013 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.
doi:10.3115/v1/s14-2077 dblp:conf/semeval/ZhuKM14 fatcat:ed6a4himtfcehcbixnh6mscxbi