We describe a Twitter sentiment analysis system developed by combining a rule-based classifier with supervised learning. We submitted our results for the message-level sub-task in SemEval 2015 Task 10, and achieved a F~1-score of 57.06%. The rule-based classifier is based on rules that are dependent on the occurrences of emoticons and opinion words in tweets. Whereas, the Support Vector Machine (SVM) is trained on semantic, dependency, and sentiment lexicon based features. The tweets are classified as positive, negative or unknown by the rule-based classifier, and as positive, negative or neutral by the SVM. The results we obtained show that rules can help refine the SVM's predictions.
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