@inproceedings{11568_686679,
 abstract = {This paper describes the CoLing Lab system for the EVALITA 2014 SENTIment POLarity Classification (SENTIPOLC) task. Our system is based on a SVM classifier trained on the rich set of lexical, global and twitter-specific features described in these pages. Overall, our system reached a 0.63 weighted F-score on the test set provided by the task organizers.},
 address = {Pisa},
 author = {Passaro, Lucia and Lebani, Gianluca and Pollacci, Laura and Chersoni, Emmanuele and Lenci, Alessandro},
 booktitle = {Proceedings of the Fourth International Workshop {{EVALITA}} 2014},
 doi = {10.12871/clicit2014215},
 isbn = {978-88-6741-472-7},
 pages = {87--92},
 publisher = {Pisa University Press},
 title = {The {{CoLing}} Lab System for Sentiment Polarity Classification of Tweets},
 year = {2014}
}