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We describe the submission of the team of the Sofia University to SemEval-2014 Task 9 on Sentiment Analysis in Twitter. We participated in subtask B, where the participating systems had to predict whether a Twitter message expresses positive, 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 general tweets, 48.37 on sarcastic tweets, and 68.24 on LiveJournal messages.

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