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AuthorAl Sallab, Ahmad
AuthorHajj, Hazem
AuthorBadaro, Gilbert
AuthorBaly, Ramy
AuthorEl Hajj, Wassim
AuthorBashir Shaban, Khaled
Available date2022-12-21T10:01:46Z
Publication Date2015
Publication Name2nd Workshop on Arabic Natural Language Processing, ANLP 2015 - held at 53rd Annual Meeting of the Association for Computational Linguistics, ACL 2015 - Proceedings
ResourceScopus
URIhttp://dx.doi.org/10.18653/v1/W15-3202
URIhttp://hdl.handle.net/10576/37500
AbstractIn 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.
Languageen
PublisherAssociation for Computational Linguistics (ACL)
SubjectClassification (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
TitleDeep learning models for sentiment analysis in arabic
TypeConference Paper
Pagination9-17
dc.accessType Abstract Only


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