Speech acts are the type of communicative acts within a conversation. Speech act recognition (aka classification) has been an active research in recent years. However, much less attention was directed for towards this task in Arabic due to the lack of resources for training an Arabic speech-act classifier. In this paper we present the Arabic speech-act corpus for Arabic tweets. A large set of 21,081 Arabic tweets in different Arabic dialects were collected, prepared and annotated for six different classes of speech-act labels, such as expression, assertion, and question. In addition, the same set of tweets were also annotated for sentiment. We aim that this corpus would promote the research for both speech-act recognition and sentiment analysis for the Arabic language, and potentially for applications that combine both tasks.
@InProceedings{ELMADANY18.22, author = {AbdelRahim Elmadany ,Hamdy Mubarak and Walid Magdy}, title = {ArSAS: An Arabic Speech-Act and Sentiment Corpus of Tweets}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Hend Al-Khalifa and King Saud University and KSA
Walid Magdy and University of Edinburgh and UK
Kareem Darwish and Qatar Computing Research Institute and Qatar
Tamer Elsayed and Qatar University and Qatar}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-25-2}, language = {english} }
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