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    <abstract>Arabizi is a form of writing Arabic text which relies on Latin letters, numerals and punctuation rather than Arabic letters. In the literature, the difficulties associated with Arabizi sentiment analysis have been underestimated, principally due to the complexity of Arabizi. In this paper, we present an approach to automatically classify sentiments of Arabizi messages into positives or negatives. In the proposed approach, Arabizi messages are first transliterated into Arabic. Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus. For corpus validation, shallow machine learning algorithms such as Support Vectors Machine (SVM) and Naive Bays (NB) are used. Simulations results demonstrate the outperformance of NB algorithm over all others. The highest achieved F1-score is up to 78% and 76% for manually and automatically transliterated dataset respectively. Ongoing work is aimed at improving the transliterator module and annotated sentiment dataset.</abstract>
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%0 Conference Proceedings
%T Arabizi sentiment analysis based on transliteration and automatic corpus annotation
%A Guellil, Imane
%A Adeel, Ahsan
%A Azouaou, Faical
%A Benali, Fodil
%A Hachani, Ala-eddine
%A Hussain, Amir
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F guellil-etal-2018-arabizi
%X Arabizi is a form of writing Arabic text which relies on Latin letters, numerals and punctuation rather than Arabic letters. In the literature, the difficulties associated with Arabizi sentiment analysis have been underestimated, principally due to the complexity of Arabizi. In this paper, we present an approach to automatically classify sentiments of Arabizi messages into positives or negatives. In the proposed approach, Arabizi messages are first transliterated into Arabic. Afterwards, we automatically classify the sentiment of the transliterated corpus using an automatically annotated corpus. For corpus validation, shallow machine learning algorithms such as Support Vectors Machine (SVM) and Naive Bays (NB) are used. Simulations results demonstrate the outperformance of NB algorithm over all others. The highest achieved F1-score is up to 78% and 76% for manually and automatically transliterated dataset respectively. Ongoing work is aimed at improving the transliterator module and annotated sentiment dataset.
%R 10.18653/v1/W18-6249
%U https://aclanthology.org/W18-6249
%U https://doi.org/10.18653/v1/W18-6249
%P 335-341

Markdown (Informal)

[Arabizi sentiment analysis based on transliteration and automatic corpus annotation](https://aclanthology.org/W18-6249) (Guellil et al., WASSA 2018)

• Arabizi sentiment analysis based on transliteration and automatic corpus annotation (Guellil et al., WASSA 2018)

ACL

• Imane Guellil, Ahsan Adeel, Faical Azouaou, Fodil Benali, Ala-eddine Hachani, and Amir Hussain. 2018. Arabizi sentiment analysis based on transliteration and automatic corpus annotation. In Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 335–341, Brussels, Belgium. Association for Computational Linguistics.