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

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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.

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Notes

  1. https://code.google.com/archive/p/word2vec/.

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Acknowledgements

This research is partially funded by Jordan University of Science and Technology, Research Grant number 20150164.

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  1. Jordan University of Science and Technology, Irbid, Jordan

    Mohammad Al-Smadi, Bashar Talafha, Mahmoud Al-Ayyoub & Yaser Jararweh

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Al-Smadi, M., Talafha, B., Al-Ayyoub, M. et al. Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int. J. Mach. Learn. & Cyber. 10, 2163–2175 (2019). https://doi.org/10.1007/s13042-018-0799-4

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