With the growth in use of social media platform, Sentiment Analysis methods become more and more popular while for classical Arabic there are many datasets available, most of the Arab countries’ Dialects suffer from very limited resources for training an automated system. In this article, we study Sentiment Analysis applied on the Tunisian Dialect and Modern Standard Arabic by providing a manually annotated dataset. On this dataset, we performed feature selection and trained 6 classifiers to provide a benchmark for future studies.
@inproceedings{acling16,
Author = {Karim Sayadi and Marcus Liwicki and Rolf Ingold and Marc Bui},
Booktitle = {To appear in the ACLing 2016 IEEE proceedings},
Date-Added = {2016-12-20 17:13:10 +0000},
Date-Modified = {2016-12-22 18:52:58 +0000},
Organization = {CICLING},
Title = {Tunisian Dialect and Modern Standard Arabic Dataset for Sentiment Analysis : Tunisian Election Context},
Year = {2016}}