EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 297 (2021) E3S Web Conf., 297 (2021) 01072 References
Open Access
Issue
E3S Web Conf.
Volume 297, 2021
The 4th International Conference of Computer Science and Renewable Energies (ICCSRE'2021)
Article Number 01072
Number of page(s) 11
DOI https://doi.org/10.1051/e3sconf/202129701072
Published online 22 September 2021
  1. M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar, M. Al-Smadi, M. Al-Ayyoub, Y. Zhao, B. Qin, and O. De Clercq. Semeval-2016 task 5: Aspect based sentiment analysis. in International workshop on semantic evaluation. 2016. [Google Scholar]
  2. S. Al-Dabet, S. Tedmori, and M. Al-Smadi, Extracting Opinion Targets Using AttentionBased Neural Model. SN Computer Science, 1: p. 10 (2020). [Google Scholar]
  3. I. Guellil, H. Saâdane, F. Azouaou, B. Gueni, and D. Nouvel, Arabic natural language processing: An overview. Journal of King Saud University - Computer and Information Sciences, (2019). [Google Scholar]
  4. A. Alawami, Aspect Terms Extraction of Arabic Dialects for Opinion Mining Using Conditional Random Fields, in Computational Linguistics and Intelligent Text Processing, A. Gelbukh, Editor. 2018, Springer International Publishing: Cham. p. 211220. [Google Scholar]
  5. D. Tang, B. Qin, and T. Liu, Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(6): p. 292–303 (2015). [Google Scholar]
  6. M. Al-Smadi, O. Qawasmeh, B. Talafha, and M. Quwaider. Human annotated arabic dataset of book reviews for aspect based sentiment analysis. in 2015 3rd International Conference on Future Internet of Things and Cloud. 2015. IEEE [Google Scholar]
  7. M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar. SemEval-2014 Task 4: Aspect Based Sentiment Analysis. in COLING 2014. 2014. [Google Scholar]
  8. I. Obaidat, R. Mohawesh, M. Al-Ayyoub, A.-S. Mohammad, and Y. Jararweh. Enhancing the determination of aspect categories and their polarities in arabic reviews using lexicon-based approaches. 2015. IEEE [Google Scholar]
  9. M. Al-Smadi, O. Qawasmeh, B. Talafha, M. Al-Ayyoub, Y. Jararweh, and E. Benkhelifa, An enhanced framework for aspect-based sentiment analysis of Hotels’ reviews: Arabic reviews case study. 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST): p. 98–103 (2016). [Google Scholar]
  10. H. ElSahar and S.R. El-Beltagy. Building large arabic multi-domain resources for sentiment analysis. 2015. Springer [Google Scholar]
  11. S. Ruder, P. Ghaffari, and J.G. Breslin, Insight-1 at semeval-2016 task 5: Deep learning for multilingual aspect-based sentiment analysis. arXiv preprint arXiv:1609.02748, (2016). [Google Scholar]
  12. A. Tamchyna and K. Veselovska. UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification. in SemEval 2016. 2016. Association for Computational Linguistics [Google Scholar]
  13. M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels' reviews. Journal of Computational Science, 27: p. 386–393 (2018). [Google Scholar]
  14. S. Al-Dabet, S. Tedmori, and M. Al-Smadi, Enhancing Arabic aspect-based sentiment analysis using deep learning models. Computer Speech & Language, 69: p. 101224 (2021). [Google Scholar]
  15. P. Gonnet and T. Deselaers. Indylstms: Independently Recurrent LSTMS. in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020. [Google Scholar]
  16. T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean, Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26: p. 3111–3119 (2013). [Google Scholar]
  17. M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, Deep contextualized word representations. arXiv preprint arXiv:1802.05365, (2018). [Google Scholar]
  18. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. in In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019. Association for Computational Linguistics [Google Scholar]
  19. J. Zhang, F.A. Liu, W. Xu, and H. Yu, Feature Fusion Text Classification Model Combining CNN and BiGRU with Multi-Attention Mechanism. Future Internet, 11(11): p. 237 (2019). [Google Scholar]
  20. L. Zhou and X. Bian. Improved text sentiment classification method based on BiGRU-Attention. in Journal of Physics: Conference Series. 2019. IOP Publishing [Google Scholar]
  21. J. Liu, Y. Yang, S. Lv, J. Wang, and H. Chen, Attention-basedBiGRU-CNNfor Chinese question classification. Journal of Ambient Intelligence and Humanized Computing: p. 1–12 (2019). [Google Scholar]
  22. M.M. Abdelgwad, T.H.A. Soliman, A.I. Taloba, and M.F. Farghaly, Arabic aspect based sentiment analysis using bidirectional GRU based models. arXiv preprint arXiv:2101.10539, (2021). [Google Scholar]
  23. A.B. Soliman, K. Eissa, and S.R. El-Beltagy, AraVec: A set of Arabic Word Embedding Models for use in Arabic NLP. Procedia Computer Science, 117: p. 256–265 (2017). [Google Scholar]
  24. A. Joulin, E. Grave, P. Bojanowski, and T. Mikolov, Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759, (2016). [Google Scholar]
  25. P.K. Sarma, Y. Liang, and W.A. Sethares, Domain adapted word embeddings for improved sentiment classification. arXiv preprint arXiv:1805.04576, (2018). [Google Scholar]
  26. P.K. Sarma, Y. Liang, and W.A. Sethares, Shallow domain adaptive embeddings for sentiment analysis. arXiv preprint arXiv:1908.06082, (2019). [Google Scholar]
  27. H. Xu, B. Liu, L. Shu, and P.S. Yu, Double embeddings andcnn-basedsequence labeling for aspect extraction. arXiv preprint arXiv:1805.04601, (2018). [Google Scholar]
  28. O. ElJundi, W. Antoun, N. El Droubi, H. Hajj, W. El-Hajj, and K. Shaban. Hulmona: The universal language model in arabic. in Proceedings of the Fourth Arabic Natural Language Processing Workshop. 2019. [Google Scholar]
  29. F. Baly and H. Hajj. AraBERT: Transformer-based model for Arabic language understanding. in Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection. 2020. [Google Scholar]
  30. W. Antoun, F. Baly, and H. Hajj, AraGPT2: Pre-Trained Transformer for Arabic Language Generation. arXiv preprint arXiv:2012.15520, (2020). [Google Scholar]
  31. A. Elnagar, Y.S. Khalifa, and A. Einea, Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications, in Intelligent Natural Language Processing: Trends and Applications, K. Shaalan, A.E. Hassanien, and F. Tolba, Editors. 2018, Springer International Publishing: Cham. p. 35–52. [Google Scholar]
  32. I.A. El-Khair, 1.5 billion words arabic corpus. arXiv preprint arXiv:1611.04033, (2016). [Google Scholar]
  33. I. Zeroual, D. Goldhahn, T. Eckart, and A. Lakhouaja. OSIAN: Open source international Arabic news corpus-preparation and integration into the CLARIN-infrastructure. in Proceedings of the Fourth Arabic Natural Language Processing Workshop, pp. 175–182. 2019. [Google Scholar]
  34. Y. Wang, G. Huang, J. Li, H. Li, Y. Zhou, and H. Jiang, Refined Global Word Embeddings Based on Sentiment Concept for Sentiment Analysis. IEEE Access, 9: p. 37075–37085 (2021). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.