National Research Council of Canada. Information and Communication Technologies
8th International Workshop on Semantic Evaluation (SemEval 2014), August 23-24 2014, Dublin, Ireland
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 SemEval-2013 sentiment 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.
Association for Computational Linguistics
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014): 443–447.