TY - GEN
T1 - A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining
AU - Badaro, Gilbert
AU - Baly, Ramy
AU - Hajj, Hazem
AU - Habash, Nizar
AU - El-Hajj, Wassim
N1 - Funding Information:
In the future, we plan to make use of this lexicon to develop more powerful SSA systems. We also plan to extend the effort to Arabic dialects and other languages. 6 Acknowledgments This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Nizar Habash performed most of his contribution to this paper while he was at the Center for Computational Learning Systems at Columbia University.
Publisher Copyright:
©2014 Association for Computational Linguistics
PY - 2014
Y1 - 2014
N2 - Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.
AB - Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.
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M3 - Conference contribution
AN - SCOPUS:85113711517
T3 - ANLP 2014 - EMNLP 2014 Workshop on Arabic Natural Language Processing, Proceedings
SP - 165
EP - 173
BT - ANLP 2014 - EMNLP 2014 Workshop on Arabic Natural Language Processing, Proceedings
A2 - Habash, Nizar
A2 - Vogel, Stephan
PB - Association for Computational Linguistics (ACL)
T2 - EMNLP 2014 Workshop on Arabic Natural Language Processing, ANLP 2014
Y2 - 25 October 2014
ER -