CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning
نویسندگان
چکیده
This paper describes the system we submitted to the SemEval-2014 shared task on sentiment analysis in Twitter. Our system is a hybrid combination of two system developed for a course project at CMUQatar. We use an SVM classifier and couple a set of features from one system with feature and parameter optimization framework from the second system. Most of the tuning and feature selection efforts were originally aimed at task-A of the shared task. We achieve an F-score of 84.4% for task-A and 62.71% for task-B and the systems are ranked 3rd and 29th respectively.
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تاریخ انتشار 2014