Abstract : Dialectal Arabic (DA) is significantly different from the Arabic language taught in schools and used in written communication and formal speech (broadcast news, religion, politics, etc.). There are many existing researches in the field of Arabic language Sentiment Analysis (SA); however, they are generally restricted to Modern Standard Arabic (MSA) or some dialects of economic or political interest. In this paper we focus on SA of the Tunisian dialect. We use Machine Learning techniques to determine the polarity of comments written in Tunisian dialect. First, we evaluate the SA systems performances with models trained using freely available MSA and Multi-dialectal data sets. We then collect and annotate a Tunisian dialect corpus of 17.000 comments from Facebook. This corpus shows a significant improvement compared to the best model trained on other Arabic dialects or MSA data. We believe that this first freely available corpus will be valuable to researchers working in the field of Tunisian Sentiment Analysis and similar areas
https://hal.archives-ouvertes.fr/hal-01592418
Contributeur : Yannick Estève
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Soumis le : dimanche 11 mars 2018 - 09:40:21
Dernière modification le : jeudi 7 février 2019 - 17:55:59
Document(s) archivé(s) le : mardi 12 juin 2018 - 12:19:32
Salima Mdhaffar, Fethi Bougares, Yannick Estève, Lamia Hadrich-Belguith. Sentiment Analysis of Tunisian Dialects: Linguistic Ressources and Experiments. Third Arabic Natural Language Processing Workshop (WANLP), Apr 2017, Valence, Spain. Third Arabic Natural Language Processing Workshop (WANLP), pp.55-61, 2017, 〈https://sites.google.com/a/nyu.edu/wanlp2017/〉. 〈10.18653/v1/W17-1307〉. 〈hal-01592418〉
