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        <title>SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)</title>
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    <name type="personal">
        <namePart type="given">Taha</namePart>
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    <name type="personal">
        <namePart type="given">Miriam</namePart>
        <namePart type="family">Fernandez</namePart>
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    <name type="personal">
        <namePart type="given">Harith</namePart>
        <namePart type="family">Alani</namePart>
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    <name type="personal">
        <namePart type="given">Sanaa</namePart>
        <namePart type="family">Sharafeddine</namePart>
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    <name type="personal">
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    <name type="personal">
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.</abstract>
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    <identifier type="doi">10.26615/978-954-452-056-4_138</identifier>
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        <url>https://aclanthology.org/R19-1138</url>
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        <date>2019-09</date>
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%0 Conference Proceedings
%T SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)
%A Tobaili, Taha
%A Fernandez, Miriam
%A Alani, Harith
%A Sharafeddine, Sanaa
%A Hajj, Hazem
%A Glavaš, Goran
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F tobaili-etal-2019-senzi
%X Arabizi is an informal written form of dialectal Arabic transcribed in Latin alphanumeric characters. It has a proven popularity on chat platforms and social media, yet it suffers from a severe lack of natural language processing (NLP) resources. As such, texts written in Arabizi are often disregarded in sentiment analysis tasks for Arabic. In this paper we describe the creation of a sentiment lexicon for Arabizi that was enriched with word embeddings. The result is a new Arabizi lexicon consisting of 11.3K positive and 13.3K negative words. We evaluated this lexicon by classifying the sentiment of Arabizi tweets achieving an F1-score of 0.72. We provide a detailed error analysis to present the challenges that impact the sentiment analysis of Arabizi.
%R 10.26615/978-954-452-056-4_138
%U https://aclanthology.org/R19-1138
%U https://doi.org/10.26615/978-954-452-056-4_138
%P 1203-1211

Markdown (Informal)

[SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)](https://aclanthology.org/R19-1138) (Tobaili et al., RANLP 2019)

• SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi) (Tobaili et al., RANLP 2019)

ACL

• Taha Tobaili, Miriam Fernandez, Harith Alani, Sanaa Sharafeddine, Hazem Hajj, and Goran Glavaš. 2019. SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi). In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1203–1211, Varna, Bulgaria. INCOMA Ltd..