[ad_1]
Collobert, R. et al. Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011).
Google Scholar
Chen, Y. Convolutional Neural Network for Sentence Classification. Master thesis (Dept. of Computer Science, University of Waterloo, 2015).
Dhuria, S. Natural language processing: An approach to parsing and semantic analysis. Int. J. New Innov. Eng. Technol. 3(1), 51–55 (2015).
Pal, A. R. & Saha, D. Word sense disambiguation: A survey. Int. J. Control Theory Comput. Model. 5(3), 1–16 (2015).
Sharma, I. & Singh, P. K. A survey on anaphora resolution. In IJCA Proceedings on Recent Innovations in Computer Science and Information Technology (RICSIT 2016), No. 1, 5–7 (2016).
Jagtap, V. S. & Pawar, K. Analysis of different approaches to sentence-level sentiment classification. Int. J. Sci. Eng. Technol. 2(3), 164–170 (2013).
Ibrahim, M. A. & Salim, N. Sentiment analysis of Arabic tweets: With special reference restaurant tweets. IJCST 4(3), 173–179 (2016).
El Gohary, A. F., Sultan, T. I., Hana, M. A. & El Dosoky, M. M. A computational approach for analyzing and detecting emotions in Arabic text. Int. J. Eng. Res. Appl. 3(3), 100–107 (2013).
Al-Saaqa, S., Abdel-Nabi, H. & Awajan, A. A survey of textual emotion detection. In The 8th International Conference on Computer Science and Information Technology (CSIT), July 11, Amman, Jordan 136–142 (IEEE, 2018).
Gupta, N. Learning distributed document representations for multi-label document categorization. Master thesis (Indian Institute of Technology, Dept. of Electrical Engineering, 2015).
El-Haj, M., Kruschwitz, U. & Fox, C. Using Mechanical Turk to create a corpus of Arabic summaries. In Language Resources (LRs) and Human Language Technologies (HLT) for Semitic Languages Workshop. The 7th International Language Resources and Evaluation Conference (LREC 2010), May 19, Valletta, Malta 36–39 (2010).
Dahou, A., Elaziz, M. A., Zhou, J. & Xiong, S. Arabic sentiment classification using convolutional neural network and differential evolution algorithm. Comput. Intell. Neurosci. 2019(2537689), 1–16 (2019).
Google Scholar
Dargan, S., Kumar, M., Ayyagari, M. R. & Kumar, G. A survey of deep learning and its applications: A new paradigm to machine learning. Arch. Comput. Methods Eng. 2020(27), 1071–1092 (2020).
Google Scholar
Al-Azani, S. & El-Alfy, E.-S. Emojis-based sentiment classification of Arabic microblogs using deep recurrent neural networks. In Proceedings of the 2018 International Conference on Computing Sciences and Engineering (ICCSE), 1–6 (IEEE, 2018).
Abbes, M., Kechaou, Z. & Alimi, A. M. Enhanced deep learning models for sentiment analysis in Arab social media. In Proceedings of the International Conference on Neural Information Processing, 667–676 (Springer, 2017).
Gulli, A. & Pal, S. Deep Learning with Keras (Packt Publishing Ltd, 2017).
Calin, O. Deep Learning Architectures (Springer International Publishing, 2020).
Google Scholar
Bengio, Y., Goodfellow, I. & Courville, A. Deep Learning Vol. 1 (MIT Press, 2016).
Google Scholar
Yadav, A. & Vishwakarma, D. K. Sentiment analysis using deep learning architectures: A review. Artif. Intell. Rev. 53(6), 4335–4385 (2020).
Google Scholar
Jang, B., Kim, M., Harerimana, G., Kang, S. U. & Kim, J. W. Bi-LSTM model to increase accuracy in text classification: Combining Word2vec CNN and attention mechanism. Appl. Sci. 10(17), 5841 (2020).
Google Scholar
Muaad, A. Y., Jayappa, H., Al-antari, M. A. & Lee, S. ArCAR: A novel deep learning computer-aided recognition for character-level Arabic text representation and recognition. Algorithms 14(7), 216 (2021).
Google Scholar
Alharbi, A. I. & Lee, M. Combining character and word embeddings for effect in Arabic informal social media microblogs. In International Conference on Applications of Natural Language to Information Systems, 213–224 (Springer, 2020).
Mäntylä, M. V., Graziotin, D. & Kuutila, M. The evolution of sentiment analysis—A review of research topics, venues, and top-cited papers. Comput. Sci. Rev. 27, 16–32 (2018).
Google Scholar
Borele, P. & Borikar, D. A. A survey on evaluating sentiments by using artificial neural network. In International Research Journal of Engineering and Technology (IRJET), Vol. 3, No. 2, 1402–1406 (2016).
Zhang, L., Wang, S. & Liu, B. Deep learning for sentiment analysis: A survey. Wiley Interdiscip. Rev. Data Min. Knowl. Disc. 8(4), 1253 (2018).
Biltawi, M., Etaiwi, W., Tedmori, S., Hudaib, A. & Awajan, A. Sentiment classification techniques for Arabic language: a survey. In International Conference on Information and Communication Systems (ICICS), April 5–7, Irbid, Jordan, 339–346, (IEEE, 2016).
Naseem, U., Razzak, I., Khan, S. K. & Prasad, M. A comprehensive survey on word representation models: From classical to state-of-the-art word representation language models. Trans. Asian Low-Resour. Lang. Inf. Process. 20(5), 1–35 (2021).
Google Scholar
Harish, B. S., Guru, D. S. & Manjunath, S. Representation and classification of text documents: a brief review. In IJCA, Special Issue on RTIPPR, Vol. 2, 110–119 (2010).
Grzegorczyk, K. Vector representations of text data in deep learning. Doctoral thesis (AGH University of Science and Technology, Faculty of Computer Science, 2018).
Babić, K., Martinčić-Ipšić, S. & Meštrović, A. Survey of neural text representation models. Information 11(11), 511 (2020).
Google Scholar
Schoot Uiterkamp, L. Improving text representations for NLP from bags to strings of words, Master thesis (University of Twente, 2019).
Mikolov, T., Chen, K., Corrado, G. & Dean, J. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).
Pennington, J., Socher, R. & Manning, C. D. Glove: global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Vol. 14, 1532–1543 (2014).
Salur, M. U. & Aydin, I. A novel hybrid deep learning model for sentiment classification. IEEE Access 8, 58080–58093 (2020).
Google Scholar
Onan, A. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr. Comput. Pract. Experience 33, 5909 (2020).
Sachin, S., Tripathi, A., Mahajan, N., Aggarwal, S. & Nagrath, P. Sentiment analysis using gated recurrent neural networks. SN Comput. Sci. 1(2), 1–13 (2020).
Google Scholar
Seo, S., Kim, C., Kim, H., Mo, K. & Kang, P. Comparative study of deep learning-based sentiment classification. IEEE Access 8, 6861–6875 (2020).
Google Scholar
Yang, L., Li, Y., Wang, J. & Sherratt, R. S. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8, 23522–23530 (2020).
Google Scholar
Elshakankery, K. & Ahmed, M. F. HILATSA: a hybrid Incremental learning approach for Arabic tweets sentiment analysis. Egypt Inform. J. 20(3), 163–171 (2019).
Google Scholar
Mohammed, A. & Kora, R. Deep learning approaches for Arabic sentiment analysis. Springer J. Soc. Netw. Anal. Min. 9(52), 1869–5469 (2019).
Oussous, A., Benjelloun, F. Z., Lahcen, A. A. & Belfkih, S. ASA: A framework for Arabic sentiment analysis. J. Inf. Sci. 46(4), 544–559 (2020).
Google Scholar
Albayati, A. Q., Al-Araji, A. S. & Ameen, S. H. Arabic sentiment analysis (ASA) using deep learning approach. J. Eng. 26(6), 85–93 (2020).
Google Scholar
Al-Azani, S. & El-Alfy, E.-S. M. Hybrid deep learning for sentiment polarity determination of Arabic microblogs. In International Conference on Neural Information Processing, November 14, Guangzhou, China, 491–500 (2017).
Alayba, A. M., Palade, V., England, M. & Iqbal, R. A combined CNN and LSTM model for Arabic sentiment analysis. In International Cross-domain Conference for Machine Learning and Knowledge Extraction, August 27, Hamburg, Germany, 179–191 (2018).
Ombabi, A. H., Ouarda, W. & Alimi, A. M. Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks. Soc. Netw. Anal. Min. 10(1), 1–13 (2020).
Google Scholar
Farha, I. A. & Magdy, W. Mazajak: an online arabic sentiment analyser. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, Italy, 192-198 (2019).
Jerbi, M. A., Achour, H. & Souissi, E. Sentiment analysis of code-switched tunisian dialect: exploring RNN-based techniques. In International Conference on Arabic Language Processing, 122–131 (Springer, 2019).
Heikal, M., Torki, M. & El-Makky, N. Sentiment analysis of arabic tweets using deep learning. Procedia Comput. Sci. 142, 114–122 (2018).
Google Scholar
Elfaik, H. & Nfaoui, E. H. Deep bidirectional LSTM network learning-based sentiment analysis for Arabic text. J. Intell. Syst. 30(1), 395–412 (2020).
Google Scholar
Albadi, N., Kurdi, M. & Mishra, S. Investigating the effect of combining GRU neural networks with handcrafted features for religious hatred detection on Arabic Twitter space. Soc. Netw. Anal. Min. 9(1), 41 (2019).
Google Scholar
El-Affendi, M. A., Alrajhi, K. & Hussain, A. A novel deep learning-based multilevel parallel attention neural (MPAN) model for multidomain Arabic sentiment analysis. IEEE Access 9, 7508–7518 (2021).
Google Scholar
Onan, A. Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks. Concurr. Comput. Pract. Experience 33(23), e5909 (2021).
Google Scholar
Onan, A. & Toçoğlu, M. A. A term weighted neural language model and stacked bidirectional LSTM based framework for sarcasm identification. IEEE Access 9, 7701–7722 (2021).
Google Scholar
Onan, A. Topic-enriched word embeddings for sarcasm identification. In Computer Science On-line Conference, 293–304 (Springer, 2019).
Omara, E., Mousa, M. & Ismail, N. Deep convolutional network for Arabic sentiment analysis. In International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC), 155–159 (IEEE, 2018).
Elnagar, A. & Einea, O. BRAD 1.0: book reviews in Arabic dataset. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), 1–8 (2016).
Ntoutsi, E. et al. Bias in data-driven artificial intelligence systems—An introductory survey. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 10(3), 1356 (2020).
Google Scholar
Roselli, D., Matthews, J. & Talagala, N. Managing bias in AI. In Companion Proceedings of The 2019 World Wide Web Conference, 539–544 (2019).
Abdulla, N. A., Ahmed, N. A., Shehab, M. A. & Al-Ayyoub, M. Arabic sentiment analysis: Lexicon-based and corpus-based. In Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference, December, 1–6 (2013).
Nabil, M., Aly, M. & Atiya, A. Astd: Arabic sentiment tweets dataset. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2515–2519 (2015).
Rosenthal, S., Farra, N. & Nakov, P. SemEval-2017 task 4: sentiment analysis in twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), 502–518, (2017).
Salameh, M., Mohammad, S. & Kiritchenko, S. Sentiment after translation: a case-study on Arabic social media posts. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 767–777 (2015).
Saleh, M. R., Valdivia, M. T. M., López, L. A. U. & Ortega, J. M. P. OCA: Opinion corpus for Arabic. J. Am. Soc. Inf. Sci. Technol. 62(10), 2045–2054 (2011).
Google Scholar
Nabil, M., Aly, M. & Atiya, A. LABR: A Large Scale Arabic Sentiment Analysis Benchmark arXiv:1411.6718 (2014).
ElSahar, H. & El-Beltagy, S.R. Building large Arabic multidomain resources for sentiment analysis. In International Conference on Intelligent Text Processing and Computational Linguistics, 23–34 (2015).
Alayba, A. M., Palade, V., England, M. & Iqbal, R. Arabic language sentiment analysis on health services. In Arabic Script Analysis and Recognition (ASAR), International Workshop, 114–118, (2017).
Elnagar, A., Khalifa, Y. S. & Einea, A. Hotel Arabic-reviews dataset construction for sentiment analysis applications. In Intelligent Natural Language Processing Trends and Applications, 35–52 (Springer, 2018).
Elmadany, A. A. & Hamdy Mubarak, W. M. ArSAS: an Arabic speech-act and sentiment corpus of tweets. In OSACT 3: The 3rd Workshop on Open-source Arabic Corpora and Processing Tools, 20 (2018).
Omara, E., Mousa, M. & Ismail, N. Deep convolutional arabic sentiment analysis with imbalanced data. In ICENCO International Computer Engineering Conference, Computer Engineering Department, Faculty of Engineering, Cairo University, 198–203 (IEEE, 2019).
Powers, D. Evaluation: From precision, recall and F-factor to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2(1), 37–63 (2011).
Google Scholar
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