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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 8/2019

01.03.2018 | Original Article

Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews

verfasst von: Mohammad Al-Smadi, Bashar Talafha, Mahmoud Al-Ayyoub, Yaser Jararweh

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 8/2019

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Abstract

This paper proposes a state-of-the-art research for aspect-based sentiment analysis of Arabic Hotels’ reviews using two implementations of long short-term memory (LSTM) neural networks. The first one is (a) a character-level bidirectional LSTM along with conditional random field classifier (Bi-LSTM-CRF) for aspect opinion target expressions (OTEs) extraction, and the second one is (b) an aspect-based LSTM for aspect sentiment polarity classification in which the aspect-OTEs are considered as attention expressions to support the sentiment polarity identification. Proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Results show that our approaches outperform baseline research on both tasks with an enhancement of 39% for the task of aspect-OTEs extraction and 6% for the aspect sentiment polarity classification task.
Vorheriger Artikel Spam analysis of big reviews dataset using Fuzzy Ranking Evaluation Algorithm and Hadoop
Nächster Artikel Sentiment analysis in teaching evaluations using sentiment phrase pattern matching (SPPM) based on association mining

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Metadaten
Titel
Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews
verfasst von
Mohammad Al-Smadi
Bashar Talafha
Mahmoud Al-Ayyoub
Yaser Jararweh
Publikationsdatum
01.03.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 8/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0799-4

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