Paper: SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter

ACL ID S14-2103
Title SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twit- ter. We participated in subtask B, where the participating systems had to predict whether a Twitter message expresses pos- itive, negative, or neutral sentiment. We trained an SVM classifier with a linear kernel using a variety of features. We used publicly available resources only, and thus our results should be easily replicable. Overall, our system is ranked 20th out of 50 submissions (by 44 teams) based on the average of the three 2014 evaluation data scores, with an F1-score of 63.62 on gen- eral tweets, 48.37 on sarcastic tweets, and 68.24 on LiveJournal messages.