Gulf Arabic Linguistic Resource Building for Sentiment Analysis

Wafia Adouane, Richard Johansson


Abstract
This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning. To our knowledge, no previous works were done for Gulf Arabic sentiment analysis despite the fact that it is present in different online platforms. Hence, the first challenge is the absence of annotated data and sentiment lexicons. To fill this gap, we created these two main linguistic resources. Then we conducted different experiments: use Naive Bayes classifier without any lexicon; add a sentiment lexicon designed basically for MSA; use only the compiled Gulf Arabic sentiment lexicon and finally use both MSA and Gulf Arabic sentiment lexicons. The Gulf Arabic lexicon gives a good improvement of the classifier accuracy (90.54 %) over a baseline that does not use the lexicon (82.81%), while the MSA lexicon causes the accuracy to drop to (76.83%). Moreover, mixing MSA and Gulf Arabic lexicons causes the accuracy to drop to (84.94%) compared to using only Gulf Arabic lexicon. This indicates that it is useless to use MSA resources to deal with Gulf Arabic due to the considerable differences and conflicting structures between these two languages.
Anthology ID:
L16-1430
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
2710–2715
Language:
URL:
https://aclanthology.org/L16-1430
DOI:
Bibkey:
Cite (ACL):
Wafia Adouane and Richard Johansson. 2016. Gulf Arabic Linguistic Resource Building for Sentiment Analysis. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2710–2715, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Gulf Arabic Linguistic Resource Building for Sentiment Analysis (Adouane & Johansson, LREC 2016)
Copy Citation:
PDF:
https://aclanthology.org/L16-1430.pdf