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    Deep learning models for sentiment analysis in arabic

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    Date
    2015
    Author
    Al Sallab, Ahmad
    Hajj, Hazem
    Badaro, Gilbert
    Baly, Ramy
    El Hajj, Wassim
    Bashir Shaban, Khaled
    ...show more authors ...show less authors
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    Abstract
    In this paper, deep learning framework is proposed for text sentiment classification in Arabic. Four different architectures are explored. Three are based on Deep Belief Networks and Deep Auto Encoders, where the input data model is based on the ordinary Bag-of-Words, with features based on the recently developed Arabic Sentiment Lexicon in combination with other standard lexicon features. The fourth model, based on the Recursive Auto Encoder, is proposed to tackle the lack of context handling in the first three models. The evaluation is carried out using Linguistic Data Consortium Arabic Tree Bank dataset, with benchmarking against the state of the art systems in sentiment classification with reported results on the same dataset. The results show high improvement of the fourth model over the state of the art, with the advantage of using no lexicon resources that are scarce and costly in terms of their development. ACL 2015. All rights reserved.
    DOI/handle
    http://dx.doi.org/10.18653/v1/W15-3202
    http://hdl.handle.net/10576/37500
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    • Computer Science & Engineering [‎2428‎ items ]

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