Paper: CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning

ACL ID S14-2028
Title CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

This paper describes the system we sub- mitted to the SemEval-2014 shared task on sentiment analysis in Twitter. Our sys- tem is a hybrid combination of two system developed for a course project at CMU- Qatar. We use an SVM classifier and cou- ple a set of features from one system with feature and parameter optimization frame- work 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 sys- tems are ranked 3rd and 29th respectively.