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المؤلفAl Sallab, Ahmad
المؤلفHajj, Hazem
المؤلفBadaro, Gilbert
المؤلفBaly, Ramy
المؤلفEl Hajj, Wassim
المؤلفBashir Shaban, Khaled
تاريخ الإتاحة2022-12-21T10:01:46Z
تاريخ النشر2015
اسم المنشور2nd Workshop on Arabic Natural Language Processing, ANLP 2015 - held at 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015 - Proceedings
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.18653/v1/W15-3202
معرّف المصادر الموحدhttp://hdl.handle.net/10576/37500
الملخصIn 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.
اللغةen
الناشرAssociation for Computational Linguistics (ACL)
الموضوعClassification (of information)
Deep learning
Information retrieval
Signal encoding
Trees (mathematics)
Auto encoders
Bag of words
Deep belief networks
Feature-based
Input datas
Learning frameworks
Learning models
Sentiment analysis
Sentiment classification
Sentiment lexicons
Sentiment analysis
العنوانDeep learning models for sentiment analysis in arabic
النوعConference Paper
الصفحات9-17
dc.accessType Abstract Only


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