SeNTU: Sentiment Analysis of Tweets by Combining a Rule-based Classifier with Supervised Learning

We describe a Twitter sentiment analysis sys-tem developed by combining a rule-based classifier with supervised learning. We sub-mitted our results for the message-level sub-task in SemEval 2015 Task 10, and achieved a F1-score of 57.06%. The rule-based classi-fier is based on rules that are dependent on the occurrences of emoticons and opinion words in tweets. Whereas, the Support Vector Ma-chine (SVM) is trained on semantic, depen-dency, 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.