CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning
Kamla Al-Mannai, Hanan Alshikhabobakr, Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor, Behrang Mohit · 2014
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 CMU-Qatar.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.