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        <title>Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification</title>
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    <name type="personal">
        <namePart type="given">Abdelghani</namePart>
        <namePart type="family">Dahou</namePart>
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    <name type="personal">
        <namePart type="given">Shengwu</namePart>
        <namePart type="family">Xiong</namePart>
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    <name type="personal">
        <namePart type="given">Junwei</namePart>
        <namePart type="family">Zhou</namePart>
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    <name type="personal">
        <namePart type="given">Mohamed</namePart>
        <namePart type="given">Houcine</namePart>
        <namePart type="family">Haddoud</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Pengfei</namePart>
        <namePart type="family">Duan</namePart>
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        <dateIssued>2016-12</dateIssued>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>With the development and the advancement of social networks, forums, blogs and online sales, a growing number of Arabs are expressing their opinions on the web. In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings. The simulation results show that the proposed scheme outperforms the existed methods on 4 out of 5 balanced and unbalanced datasets.</abstract>
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        <url>https://aclanthology.org/C16-1228</url>
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        <date>2016-12</date>
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%0 Conference Proceedings
%T Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
%A Dahou, Abdelghani
%A Xiong, Shengwu
%A Zhou, Junwei
%A Haddoud, Mohamed Houcine
%A Duan, Pengfei
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F dahou-etal-2016-word
%X With the development and the advancement of social networks, forums, blogs and online sales, a growing number of Arabs are expressing their opinions on the web. In this paper, a scheme of Arabic sentiment classification, which evaluates and detects the sentiment polarity from Arabic reviews and Arabic social media, is studied. We investigated in several architectures to build a quality neural word embeddings using a 3.4 billion words corpus from a collected 10 billion words web-crawled corpus. Moreover, a convolutional neural network trained on top of pre-trained Arabic word embeddings is used for sentiment classification to evaluate the quality of these word embeddings. The simulation results show that the proposed scheme outperforms the existed methods on 4 out of 5 balanced and unbalanced datasets.
%U https://aclanthology.org/C16-1228
%P 2418-2427

Markdown (Informal)

[Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification](https://aclanthology.org/C16-1228) (Dahou et al., COLING 2016)

• Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification (Dahou et al., COLING 2016)

ACL

• Abdelghani Dahou, Shengwu Xiong, Junwei Zhou, Mohamed Houcine Haddoud, and Pengfei Duan. 2016. Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2418–2427, Osaka, Japan. The COLING 2016 Organizing Committee.