The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews
Abstract
Abstract In this research, state-of-the-art approaches based on supervised machine learning are presented to address the challenges of aspect-based sentiment analysis (ABSA) of Arabic Hotels’ reviews. Two approaches of deep recurrent neural network (RNN) and support vector machine (SVM) are implemented and trained along with lexical, word, syntactic, morphological, and semantic features. The proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Evaluation results show that the SVM approach outperforms the other deep RNN approach in the research investigated tasks (T1: aspect category identification, T2: aspect opinion target expression (OTE) extraction, and T3: aspect sentiment polarity identification). Whereas, when focusing on the execution time required for training and testing the models, the deep RNN execution time was faster, especially for the second task.
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Cited by
A survey on sentiment analysis methods, applications, and challenges
Wankhade, M., Rao, A. C. S., & Kulkarni, C. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10144-1 · Cited by 1,528
Sentiment analysis using deep learning architectures: a review
Yadav, A., & Vishwakarma, D. K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09794-5 · Cited by 841
Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
Do, H. H., Prasad, P., Maag, A., & Alsadoon, A. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.10.003 · Cited by 614
A survey on aspect-based sentiment classification
Brauwers, G., & Frasincar, F. (2023) · ACM Computing Surveys · doi:10.1145/3503044 · Cited by 165
Aspect-based Sentiment Analysis of Scientific Reviews
Chakraborty, S., Goyal, P., & Mukherjee, A. (2020) · Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 · doi:10.1145/3383583.3398541 · Cited by 63
Metadata
- Authors
- Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B.
- Venue
- Journal of Computational Science
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 27
- Pages
- 386-393
- ISSN
- 1877-7503 (ISSN-L)
- OpenAlex
- W2769317996
- Cited by
- 339
- References
- 69
- FWCI
- 17.88
- Type
- journal article
- Language
- en
- Open access
- closed
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- Al-Smadi, M., Qawasmeh, O., Al-Ayyoub, M., Jararweh, Y., & Gupta, B. (2018). Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science. https://doi.org/10.1016/j.jocs.2017.11.006
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