Kea: Expression-level Sentiment Analysis from Twitter Data
نویسندگان
چکیده
This paper describes an expression-level sentiment detection system that participated in the subtask A of SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system uses a supervised approach to learn the features from the training data to classify expressions in new tweets as positive, negative or neutral. The proposed approach helps to understand the relevant features that contribute most in this classification task.
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