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Aspect-based sentiment analysis: an overview in the use of Arabic language

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Abstract

Sentiment analysis has become one of the most active research areas in natural language processing, and the Arabic language retains its importance in this field. It is so because of the increased use of Arabic on the internet that pushes many users to share their views or thoughts about certain products and services. Despite its crucial importance, most of the existing Arabic sentiment analysis studies have been performed on document or sentence levels with little attention to the aspect level. However, the aspect level’s main objective, also known as aspect-based sentiment analysis, is to extract the discussed aspects and identify their related sentiment polarities from a given review or text. The result is to provide more detailed information than general sentiment analysis. Therefore, this paper seeks to provide a comprehensive review of the Arabic aspect-based sentiment analysis studies and highlights the main challenges that face the different proposed approaches. The relevant gaps in the current literature and the future research directions in this area are also discussed. This survey can guide future researchers who want to contribute to the improvement of this domain.

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Notes

  1. http://www.internetworldstats.com/stats7.htm.

References

  • Abas AR, El-Henawy I, Mohamed H, Abdellatif A (2020) Deep learning model for fine-grained aspect-based opinion mining. IEEE Access 8:128845–128855. https://doi.org/10.1109/ACCESS.2020.3008824

    Article  Google Scholar 

  • Abdelgwad M, Soliman MA, I.Taloba TH, Farghaly MF (2021) Arabic aspect based sentiment analysis using bidirectional GRU based models. J King Saud Univ Comput Inform Sci. https://doi.org/10.1016/j.jksuci.2021.08.030

    Article  Google Scholar 

  • Abd-Elhamid L, Elzanfaly D, Eldin AS (2016) Feature-based sentiment analysis in online Arabic reviews. In: 2016 11th International conference on computer engineering & systems (ICCES), pp 260–265

  • Abdul-Mageed M, Diab M, Korayem M (2011) Subjectivity and sentiment analysis of modern standard Arabic. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 587–591

  • Abu Hammad AS (2013) An approach for detecting spam in Arabic opinion reviews

  • Akhtar MS, Ekbal A, Cambria E (2020a) How intense are you? Predicting intensities of emotions and sentiments using stacked ensemble [application notes]. IEEE Comput Intell Mag 15(1):64–75

    Article  Google Scholar 

  • Akhtar MS, Garg T, Ekbal A (2020b) Multi-task learning for aspect term extraction and aspect sentiment classification. Neurocomputing 398:247–256. https://doi.org/10.1016/j.neucom.2020.02.093

    Article  Google Scholar 

  • Alassaf M, Qamar AM (2020a) Aspect-based sentiment analysis of Arabic tweets in the education sector using a hybrid feature selection method. In: 2020 14th International conference on innovations in information technology (IIT), pp 178–185

  • Alassaf M, Qamar AM (2020b) Improving sentiment analysis of Arabic tweets by one-way ANOVA. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2020.10.023

    Article  Google Scholar 

  • Alawami A (2018) Aspect terms extraction of Arabic dialects for opinion mining using conditional random fields. In: Gelbukh A (ed) Computational linguistics and intelligent text processing, vol 9624. Springer, Cham, pp 211–220. https://doi.org/10.1007/978-3-319-75487-1_16

    Chapter  Google Scholar 

  • Al-Ayyoub M, Nuseir A, Kanaan G, Al-Shalabi R (2016) Hierar-chical classifiers for multi-way sentiment analysis of arabic reviews. Int J Adv Comput Sci Appl (IJACSA) 7(2):531–539

    Google Scholar 

  • Al-Ayyoub M, Gigieh AH, Al-Qwaqenah AA, Al-Kabi M, Talafhah B, Alsmadi I (2018) Aspect-based sentiment analysis of Arabic laptop reviews. Available at https://www.semanticscholar.org/paper/Aspect-Based-Sentiment-Analysis-of-Arabic-Laptop-Al-Ayyoub-Gigieh/0b91daad3743927b5307c747652b855b9715544d

  • Al-Ayyoub M, Khamaiseh AA, Jararweh Y, Al-Kabi MN (2019) A comprehensive survey of arabic sentiment analysis. Inf Process Manag 56(2):320–342. https://doi.org/10.1016/j.ipm.2018.07.006

    Article  Google Scholar 

  • Al-Dabet S, Tedmori S, Al-Smadi M (2020) Extracting opinion targets using attention-based neural model. SN Comput Sci 1:10. https://doi.org/10.1007/s42979-020-00270-4

    Article  Google Scholar 

  • Al-Dabet S, Tedmori S, Al-Smadi M (2021) Enhancing Arabic aspect-based sentiment analysis using deep learning models. Comput Speech Lang 69:101224. https://doi.org/10.1016/j.csl.2021.101224

    Article  Google Scholar 

  • Aldayel HK, Azmi AM (2016) Arabic tweets sentiment analysis—a hybrid scheme. J Inform Sci 42(6):782–797. https://doi.org/10.1177/0165551515610513

    Article  Google Scholar 

  • Alharbi LM, Qamar AM (2021) Arabic sentiment analysis of eateries’ reviews: Qassim region Case study. In: 2021 National Computing Colleges Conference (NCCC), pp 1–6

  • Ali MM (2021) Arabic sentiment analysis about online learning to mitigate covid-19. J Intell Syst 30(1):524–540

    Google Scholar 

  • Aljabri M, Chrouf SM, Alzahrani NA, Alghamdi L, Alfehaid R, Alqarawi R, Alhuthayfi J, Alduhailan N (2021) Sentiment analysis of Arabic tweets regarding distance learning in Saudi Arabia during the COVID-19 pandemic. Sensors 21(16):5431

    Article  Google Scholar 

  • Alkadri A, Elkorany A (2016) Semantic feature based Arabic opinion mining using ontology. Int J Adv Comput Sci Appl 7:577. https://doi.org/10.14569/IJACSA.2016.070576

    Article  Google Scholar 

  • Al-Moslmi T, Albared M, Al-Shabi A, Omar N, Abdullah S (2018) Arabic senti-lexicon: constructing publicly available language resources for Arabic sentiment analysis. J Inform Sci 44(3):345–362. https://doi.org/10.1177/0165551516683908

    Article  Google Scholar 

  • Alqudsi A, Omar N, Shaker K (2014) Arabic machine translation: a survey. Artif Intell Rev 42(4):549–572

    Article  Google Scholar 

  • AlSalman H (2020) An improved approach for sentiment analysis of Arabic tweets in twitter social media. In: 2020 3rd International conference on computer applications & information security (ICCAIS), pp 1–4

  • Al-Sarhan H, Al-So’ud M, Al-Smadi M, Al-Ayyoub M, Jararweh Y (2016) Framework for affective news analysis of arabic news: 2014 gaza attacks case study. In: 2016 7th International Conference on Information and Communication Systems (ICICS), pp 327–332

  • Al-Smadi M, Qawasmeh O, Talafha B, Quwaider M (2015) Human annotated arabic dataset of book reviews for aspect based sentiment analysis. In: 2015 3rd International conference on future internet of things and cloud, pp 726–730

  • Al-Smadi M, Qawasmeh O, Talafha B, Al-Ayyoub M, Jararweh Y, Benkhelifa E (2016) An enhanced framework for aspect-based sentiment analysis of Hotels’ reviews: Arabic reviews case study. In: 2016 11th International Conference for Internet Technology and Secured Transactions (ICITST), pp 98–103

  • 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. J Comput Sci 27:386–393. https://doi.org/10.1016/j.jocs.2017.11.006

    Article  Google Scholar 

  • Al-Smadi M, Al-Ayyoub M, Jararweh Y, Qawasmeh O (2019a) Enhancing aspect-based sentiment analysis of Arabic Hotels’ reviews using morphological, syntactic and semantic features. Inf Process Manag 56(2):308–319. https://doi.org/10.1016/j.ipm.2018.01.006

    Article  Google Scholar 

  • Al-Smadi M, Talafha B, Al-Ayyoub M, Jararweh Y (2019b) Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int J Mach Learn Cybernet 10(8):2163–2175

    Article  Google Scholar 

  • Al-Smadi M, Hammad MM, SaA Al-Zboon, Al-Tawalbeh S, Cambria E (2021) Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2021.107540

    Article  Google Scholar 

  • Al-Twairesh N, Al-Khalifa H, Al-Salman A (2014) Subjectivity and sentiment analysis of Arabic: trends and challenges. In: 2014 IEEE/ACS 11th international conference on computer systems and applications (AICCSA), pp 148–155

  • Alwaneen TH, Azmi AM, Aboalsamh HA, Cambria E, Hussain A (2021) Arabic question answering system: a survey. Artif Intell Rev 55(1):207–253

    Article  Google Scholar 

  • Aly M, Atiya A (2013) LABR: a large scale Arabic book reviews dataset. ACL, pp 494–498

  • Areed S, Alqaryouti O, Siyam B, Shaalan K (2020) Aspect-based sentiment analysis for Arabic government reviews. In: Abd Elaziz M, Al-qaness MAA, Ewees AA, Dahou A (eds) Recent advances in NLP: the case of Arabic language. Springer, Cham, pp 143–162. https://doi.org/10.1007/978-3-030-34614-0_8

    Chapter  Google Scholar 

  • Arican BN, Özçelik M, Aslan DB, Sarmış E, Parlar S, Yıldız OT (2021) Creating domain dependent Turkish wordnet and sentinet. In: Proceedings of the 11th Global Wordnet Conference, pp 243–251

  • Arulmurugan R, Sabarmathi K, Anandakumar H (2019) Classification of sentence level sentiment analysis using cloud machine learning techniques. Cluster Comput 22(1):1199–1209

    Article  Google Scholar 

  • Asghar MZ, Ullah A, Ahmad S, Khan A (2020) Opinion spam detection framework using hybrid classification scheme. Soft Comput 24(5):3475–3498

    Article  Google Scholar 

  • Ashi MM, Siddiqui MA, Nadeem F (2019) Pre-trained word embeddings for Arabic aspect-based sentiment analysis of airline tweets. In: Advances in intelligent systems and computing. Springer, Cham, pp 241–251

    Google Scholar 

  • Aydin CR, Güngör T (2020) Combination of recursive and recurrent neural networks for aspect-based sentiment analysis using inter-aspect relations. IEEE Access 8:77820–77832

    Article  Google Scholar 

  • Aydın CR, Güngör T (2021) Sentiment analysis in Turkish: supervised, semi-supervised, and unsupervised techniques. Nat Lang Eng 27(4):455–483

    Article  Google Scholar 

  • Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Lrec, pp 2200–2204

  • Badaro G, Baly R, Hajj H, El-Hajj W, Shaban KB, Habash N, Al-Sallab A, Hamdi A (2019) A survey of opinion mining in Arabic: a comprehensive system perspective covering challenges and advances in tools, resources, models, applications, and visualizations. ACM Trans Asian Low Resour Lang Inform Process 18(3):1–52. https://doi.org/10.1145/3295662

    Article  Google Scholar 

  • Baly F, Hajj H (2020) 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, pp 9–15

  • Baly R, Khaddaj A, Hajj H, El-Hajj W, Shaban KB (2019) Arsentd-lev: a multi-topic corpus for target-based sentiment analysis in arabic levantine tweets. arXiv preprint arXiv:1906.01830

  • Barnes J, Velldal E, Øvrelid L (2021) Improving sentiment analysis with multi-task learning of negation. Nat Lang Eng 27(2):249–269

    Article  Google Scholar 

  • Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR (2021) ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst 115:279–294

    Article  Google Scholar 

  • Biltawi M, Al-Naymat G, Tedmori S (2017) Arabic sentiment classification: a hybrid approach, pp 104–108

  • Boudad N, Faizi R, Oulad Haj Thami R, Chiheb R (2018) Sentiment analysis in Arabic: a review of the literature. Ain Shams Eng J 9(4):2479–2490. https://doi.org/10.1016/j.asej.2017.04.007

    Article  Google Scholar 

  • Cambria E, Poria S, Hazarika D, Kwok K (2018) SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of the AAAI conference on artificial intelligence

  • Cambria E, Li Y, Xing FZ, Poria S, Kwok K (2020) SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis. In: Proceedings of the 29th ACM international conference on information & knowledge management, pp 105–114

  • Cassab S, Kurdy M-B (2020) Ontology-based emotion detection in Arabic social media. Int J Eng Res Technol (IJERT) 9(08):1991–2013

    Google Scholar 

  • Chen H-Y, Chen H-H (2016) Implicit polarity and implicit aspect recognition in opinion mining. ACL 2016, pp 20–25

  • Choi G, Oh S, Kim H (2020) Improving document-level sentiment classification using importance of sentences. Entropy 22(12):1336

    Article  Google Scholar 

  • Dashtipour K, Raza A, Gelbukh A, Zhang R, Cambria E, Hussain A (2019) Persent 2.0: Persian sentiment lexicon enriched with domain-specific words. In: International Conference on Brain Inspired Cognitive Systems, pp. 497–509

  • Dashtipour K, Gogate M, Cambria E, Hussain A (2021) A novel context-aware multimodal framework for Persian sentiment analysis. Neurocomputing 457:377–388

    Article  Google Scholar 

  • Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, Volume1 (Long and Short Papers), pp 4171–4186

  • Do HH, Prasad PWC, Maag A, Alsadoon A (2019) Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst Appl 118:272–299. https://doi.org/10.1016/j.eswa.2018.10.003

    Article  Google Scholar 

  • Dosoula N, Griep R, Ridder D, Slangen R, Schouten R, Frasincar F (2016) Detection of multiple implicit features per sentence in consumer review data, pp 289–303

  • Duwairi RM, El-Orfali M (2014) A study of the effects of preprocessing strategies on sentiment analysis for Arabic text. J Inf Sci 40(4):501–513. https://doi.org/10.1177/0165551514534143

    Article  Google Scholar 

  • El-Alami F-Z, Alaoui E, Nahnahi NE (2021) Contextual semantic embeddings based on fine-tuned AraBERT model for Arabic text multi-class categorization. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.02.005

    Article  Google Scholar 

  • Elarnaoty M, AbdelRahman S, Fahmy A (2012) A machine learning approach for opinion holder extraction in Arabic language. arXiv preprint arXiv:1206.1011

  • Eldin SS, Mohammed A, Hefny H, Ahmed ASE (2019) An enhanced opinion retrieval approach on Arabic text for customer requirements expansion. J King Saud Univ Comput Inf Sci 33:351–363

    Google Scholar 

  • Eldin SS, Mohammed A, Eldin AS, Hefny H (2020) An enhanced opinion retrieval approach via implicit feature identification. J Intell Inf Syst. https://doi.org/10.1007/s10844-020-00622-9

    Article  Google Scholar 

  • El-Halees A (2012) Opinion mining from Arabic comparative sentences, pp 265-271

  • El-Halees AM, Salah D (2018) Feature-based opinion summarization for Arabic reviews. In: 2018 International Arab conference on information technology (ACIT), pp 1–5

  • ElJundi O, Antoun W, El Droubi N, Hajj H, El-Hajj W, Shaban K (2019) hulmona: The universal language model in Arabic. In: Proceedings of the Fourth Arabic Natural Language Processing Workshop, pp 68–77

  • El-Masri M, Altrabsheh N, Mansour H (2017) Successes and challenges of Arabic sentiment analysis research: a literature review. Social Netw Anal Min 7(1):1–22

    Article  Google Scholar 

  • ElSahar H, El-Beltagy SR (2015) Building large Arabic multi-domain resources for sentiment analysis, pp 23–34

  • El Mekki A, El Mahdaouy A, Berrada I, Khoumsi A (2021) Domain adaptation for Arabic cross-domain and cross-dialect sentiment analysis from contextualized word embedding. In: Proceedings of the 2021 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, pp 2824–2837

  • Farra N, McKeown K (2017) SMARTies: sentiment models for Arabic target entities. EACL 2017, pp 1002–1013

  • Farra N, McKeown K, Habash N (2015) Annotating targets of opinions in Arabic using crowdsourcing, pp 89–98

  • Farha IA, Zaghouani W, Magdy W (2021) Overview of the wanlp 2021 shared task on sarcasm and sentiment detection in Arabic. In: Proceedings of the Sixth Arabic Natural Language Processing Workshop, pp 296–305

  • Feifei L, Fan W, Zhongqing W (2021) Sentiment analysis with reviewer types and generative adversarial network. Data Anal Knowl Discov 5(4):72–79

    Google Scholar 

  • Geetha M, Renuka DK (2021) Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model. Int J Intell Netw 2:64–69

    Google Scholar 

  • Goldberg Y (2016) A primer on neural network models for natural language processing. J Artif Intell Res 57:345–420. https://doi.org/10.1613/jair.4992

    Article  MathSciNet  MATH  Google Scholar 

  • González J, Hurtado L-F, Pla F (2020) Self-attention for Twitter sentiment analysis in Spanish. J Intell Fuzzy Syst 39(2):2165–2175

    Article  Google Scholar 

  • Guellil I, Saâdane H, Azouaou F, Gueni B, Nouvel D (2019) Arabic natural language processing: an overview. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2019.02.006

    Article  Google Scholar 

  • Halim Z, Waqar M, Tahir M (2020) A machine learning-based investigation utilizing the in-text features for the identification of dominant emotion in an email. Knowl Based Syst 208:106443

    Article  Google Scholar 

  • Han Y, Liu M, Jing W (2020) Aspect-level drug reviews sentiment analysis based on double BiGRU and knowledge transfer. IEEE Access 8:21314–21325

    Article  Google Scholar 

  • Heikal M, Torki M, El-Makky N (2018) Sentiment analysis of Arabic Tweets using deep learning. Procedia Comput Sci 142:114–122

    Article  Google Scholar 

  • Helwe C, Dib G, Shamas M, Elbassuoni S (2020) A semi-supervised BERT approach for Arabic named entity recognition. In: Proceedings of the Fifth Arabic Natural Language Processing Workshop, pp 49–57

  • Howard J, Ruder S (2018) Universal language model fine-tuning for text classification. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 328–339

  • Hu M, Liu B (2004) Mining opinion features in customer reviews, pp 755–760

  • Ibrahim MA, Salim N (2016) Aspect oriented sentiment analysis model of Arabic tweets. Int J Comput Sci Trends Technol 4(4):342–353

    Google Scholar 

  • Ismail S, Alsammak A, Elshishtawy T (2016) A generic approach for extracting aspects and opinions of Arabic reviews, pp 173–179

  • John GH, Kohavi R, Pfleger K (1994) Irrelevant features and the subset selection problem. In: Machine learning Proceedings 1994. Elsevier, Amsterdam, pp 121–129

    Chapter  Google Scholar 

  • Kumar A, Kohail S, Kumar A, Ekbal A, Biemann C (2016) Iit-tuda at semeval-2016 task 5: beyond sentiment lexicon: combining domain dependency and distributional semantics features for aspect based sentiment analysis. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 1129–1135

  • Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data, pp 282–289

  • Lakizadeh A, Zinaty Z (2021) A novel hierarchical attention-based method for aspect-level sentiment classification. J AI Data Min 9(1):87–97

    Google Scholar 

  • Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: Proceedings of the 2016 conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, pp 260–270

  • Li X, Bing L, Lam W, Shi B (2018) Transformation networks for target-oriented sentiment classification. ACL 2018, pp 946–956

  • Liang B, Su H, Gui L, Cambria E, Xu R (2022) Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks. Knowl Based Syst 235:107643. https://doi.org/10.1016/j.knosys.2021.107643

    Article  Google Scholar 

  • Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 5(1):1–167. https://doi.org/10.2200/s00416ed1v01y201204hlt016

    Article  Google Scholar 

  • Ma D, Li S, Zhang X, Wang H (2017) Interactive attention networks for aspect-level sentiment classification. arXiv preprint arXiv:1709.00893

  • Martens G, De Greve L, Singh P, Van Hee C, Lefever E (2021) Aspect-based Sentiment analysis for German: analyzing “talk of literature” surrounding literary prizes on social media. Computational Linguistics in The Netherlands (CLIN 31)

  • Mataoui MH, Hacine TEB, Tellache I, Bakhtouchi A, Zelmati O (2018) A new syntax-based aspect detection approach for sentiment analysis in Arabic reviews, pp 1–6

  • Mihalcea R, Banea C, Wiebe J (2007) Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th annual meeting of the association of computational linguistics, pp 976–983

  • Mohammad A-S, Al-Ayyoub M, Al-Sarhan H, Jararweh Y (2015) Using aspect-based sentiment analysis to evaluate arabic news affect on readers. In: 2015 IEEE/ACM 8th international conference on utility and cloud computing (UCC), pp 436–441

  • Mohammad AS, Al-Ayyoub M, Al-Sarhan HN, Jararweh Y (2016) An aspect-based sentiment analysis approach to evaluating arabic news affect on readers. J Univers Comput Sci 22(5):630–649

    MathSciNet  Google Scholar 

  • Mohit B, Rozovskaya A, Habash N, Zaghouani W, Obeid O (2014) The first QALB shared task on automatic text correction for Arabic. ANLP@EMNLP

  • Naik M, Vasumathi D, Kumar A (2021) An adaptable scheme to enhance the sentiment classification of Telugu language. Social Netw Anal Min 11(1):1–12

    Article  Google Scholar 

  • Obaidat I, Mohawesh R, Al-Ayyoub M, Mohammad A-S, Jararweh Y (2015) Enhancing the determination of aspect categories and their polarities in Arabic reviews using lexicon-based approaches, pp 1–6

  • Ombabi AH, Ouarda W, Alimi AM (2020) Deep learning CNN–LSTM framework for Arabic sentiment analysis using textual information shared in social networks. Social Netw Anal Min 10(1):53. https://doi.org/10.1007/s13278-020-00668-1

    Article  Google Scholar 

  • Oueslati O, Cambria E, HajHmida MB, Ounelli H (2020) A review of sentiment analysis research in Arabic language. Futur Gener Comput Syst 112:408–430. https://doi.org/10.1016/j.future.2020.05.034

    Article  Google Scholar 

  • Pathak A, Kumar S, Roy PP, Kim B-G (2021) Aspect-based sentiment analysis in Hindi language by ensembling pre-trained mBERT models. Electronics 10(21):2641

    Article  Google Scholar 

  • Peng H, Ma Y, Poria S, Li Y, Cambria E (2021) Phonetic-enriched text representation for Chinese sentiment analysis with reinforcement learning. Inform Fusion 70:88–99

    Article  Google Scholar 

  • Pontiki M, Galanis D, Pavlopoulos J, Papageorgiou H, Androutsopoulos I, Manandhar S (2014) SemEval-2014 task 4: aspect based sentiment analysis. COLING 2014, pp 122–123

  • Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, De Clercq O (2016) Semeval-2016 task 5: aspect based sentiment analysis. In: International workshop on semantic evaluation, pp 19–30

  • Ray B, Garain A, Sarkar R (2021) An ensemble-based hotel recommender system using sentiment analysis and aspect categorization of hotel reviews. Appl Soft Comput 98:106935

    Article  Google Scholar 

  • Refaee E, Rieser V (2014) An arabic twitter corpus for subjectivity and sentiment analysis. LREC, pp 2268–2273

  • Regatte YR, Gangula RRR, Mamidi R (2020) Dataset creation and evaluation of aspect based sentiment analysis in Telugu, a low resource language. In: Proceedings of the 12th language resources and evaluation conference, pp 5017–5024

  • Rhanoui M, Mikram M, Yousfi S, Barzali S (2019) A CNN-BiLSTM model for document-level sentiment analysis. Mach Learn Knowl Extr 1(3):832–847

    Article  Google Scholar 

  • Rojas-Barahona LM (2016) Deep learning for sentiment analysis. Lang Linguistics Compass 10(12):701–719. https://doi.org/10.1111/lnc3.12228

    Article  Google Scholar 

  • Ruder S, Ghaffari P, Breslin JG (2016) Insight-1 at semeval-2016 task 5: deep learning for multilingual aspect-based sentiment analysis. arXiv preprint arXiv:1609.02748

  • Sadr H, Pedram MM, Teshnehlab M (2019) A robust sentiment analysis method based on sequential combination of convolutional and recursive neural networks. Neural Process Lett 50(3):2745–2761

    Article  Google Scholar 

  • Salameh M, Mohammad S, Kiritchenko S (2015) 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, pp 767–777

  • Salur MU, Aydin I (2020) A novel hybrid deep learning model for sentiment classification. IEEE Access 8:58080–58093

    Article  Google Scholar 

  • Sánchez-Holgado P, Arcila-Calderón C (2020) Supervised sentiment analysis of science topics: developing a training set of tweets in Spanish. J Inform Technol Res (JITR) 13(3):80–94

    Article  Google Scholar 

  • Shoukry AM (2013) Arabic sentence-level sentiment analysis

  • Shoukry A, Rafea A (2012) Sentence-level Arabic sentiment analysis. In: 2012 International conference on collaboration technologies and systems (CTS), pp 546–550

  • Smetanin S, Komarov M (2021) Deep transfer learning baselines for sentiment analysis in Russian. Inf Process Manag 58(3):102484

    Article  Google Scholar 

  • Socher R, Chen D, Manning CD, Ng A (2013) Reasoning with neural tensor networks for knowledge base completion. Adv Neural Inf Process Syst 26:926–934

    Google Scholar 

  • Soliman AB, Eissa K, El-Beltagy SR (2017) AraVec: a set of Arabic word embedding models for use in Arabic NLP. Procedia Comput Sci 117:256–265. https://doi.org/10.1016/j.procs.2017.10.117

    Article  Google Scholar 

  • Song W, Wen Z, Xiao Z, Park SC (2021) Semantics perception and refinement network for aspect-based sentiment analysis. Knowl Based Syst 214:106755

    Article  Google Scholar 

  • Srifi M, Oussous A, Ait Lahcen A, Mouline S (2021) Evaluation of recent advances in recommender systems on Arabic content. J Big Data 8(1):35. https://doi.org/10.1186/s40537-021-00420-2

    Article  Google Scholar 

  • Strapparava C, Valitutti A (2004) Wordnet affect: an affective extension of wordnet. Lrec

  • Su J, Tang J, Jiang H, Lu Z, Ge Y, Song L, Xiong D, Sun L, Luo J (2021) Enhanced aspect-based sentiment analysis models with progressive self-supervised attention learning. Artif Intell 296:103477

    Article  MathSciNet  MATH  Google Scholar 

  • Tamchyna A, Veselovská K (2016) UFAL at SemEval-2016 task 5: recurrent neural networks for sentence classification. SemEval 2016, pp 367–371

  • Tang D, Qin B, Liu T (2015) Deep learning for sentiment analysis: successful approaches and future challenges. Wiley Interdiscip Rev Data Min Knowl Discov 5(6):292–303. https://doi.org/10.1002/widm.1171

    Article  Google Scholar 

  • Tang D, Qin B, Liu T (2016) Aspect level sentiment classification with deep memory network, pp 214–224

  • Touahri I, Mazroui A (2021) Deep analysis of an Arabic sentiment classification system based on lexical resource expansion and custom approaches building. Int J Speech Technol 24(1):109–126

    Article  Google Scholar 

  • Touati I, Graja M, Ellouze M, Belguith LH (2018) Opinion target extraction from Arabic news articles using shallow features, pp 113–118

  • Trigui S, Boujelben I, Jamoussi S, Ayed YB (2019) ADAL system: aspect detection for Arabic language. In: International conference on hybrid intelligent systems, pp 31–40

  • Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. arXiv preprint cs/0212032

  • Valdivia A, Luzón MV, Cambria E, Herrera F (2018) Consensus vote models for detecting and filtering neutrality in sentiment analysis. Inform Fusion 44:126–135. https://doi.org/10.1016/j.inffus.2018.03.007

    Article  Google Scholar 

  • Van Thin D, Nguyen NL-T, Truong TM, Le LS, Vo DT (2021) Two new large corpora for Vietnamese aspect-based sentiment analysis at sentence level. Trans Asian Low Resour Lang Inf Process 20(4):1–22

    Article  Google Scholar 

  • Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based LSTM for aspect-level sentiment classification. EMNLP 2016, pp 606–615

  • Wang Z, Ho S-B, Cambria E (2020) Multi-level fine-scaled sentiment sensing with ambivalence handling. Int J Uncertain Fuzziness Knowl Based Syst 28(4):683

    Article  Google Scholar 

  • Wang Y, Chen Q, Shen J, Hou B, Ahmed M, Li Z (2021a) Aspect-level sentiment analysis based on gradual machine learning. Knowl Based Syst 212:106509

    Article  Google Scholar 

  • Wang X, Xu G, Zhang Z, Jin L, Sun X (2021b) End-to-end aspect-based sentiment analysis with hierarchical multi-task learning. Neurocomputing 455:178–188

    Article  Google Scholar 

  • Wiegand M, Bocionek C, Ruppenhofer J (2016) Opinion holder and target extraction on opinion compounds–a linguistic approach. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies, pp 800–810

  • Wijayanti R, Arisal A (2021) Automatic Indonesian sentiment lexicon curation with sentiment valence tuning for social media sentiment analysis. ACM Trans Asian Low Resour Lang Inf Process (TALLIP) 20(1):1–16

    Article  Google Scholar 

  • Xue W, Li T (2018) Aspect based sentiment analysis with gated convolutional networks. ACL 2018, pp 2514–2523

  • Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019a) XLNet: generalized autoregressive pretraining for language understanding. Adv Neural Inf Process Syst 32:5753–5763

    Google Scholar 

  • Yang M, Jiang Q, Shen Y, Wu Q, Zhao Z, Zhou W (2019b) Hierarchical human-like strategy for aspect-level sentiment classification with sentiment linguistic knowledge and reinforcement learning. Neural Netw 117:240–248

    Article  Google Scholar 

  • Zaghouani W, Mohit B, Habash N, Obeid O, Tomeh N, Rozovskaya A, Farra N, Alkuhlani S, Oflazer K (2014) Large scale Arabic error annotation: guidelines and framework. In: 9th International conference on language resources and evaluation, LREC 2014, pp 2362–2369

  • Zhang M-l, Zhou Z-h (2005) Multi-label neural networks with applications to functional genomics and text categorization. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.130.7318

  • Zhang Y, Du J, Ma X, Wen H, Fortino G (2021a) Aspect-based sentiment analysis for user reviews. Cogn Comput 13:1114–1127

    Article  Google Scholar 

  • Zhang J, Li F, Zhang Z, Xu G, Wang Y, Wang X, Zhang Y (2021b) Integrate syntax information for target-oriented opinion words extraction with target-specific graph convolutional network. Neurocomputing 440:321–335. https://doi.org/10.1016/j.neucom.2020.07.152

    Article  Google Scholar 

  • Zhao A, Yu Y (2021) Knowledge-enabled BERT for aspect-based sentiment analysis. Knowl Based Syst 227:107220

    Article  Google Scholar 

  • Zhao N, Gao H, Wen X, Li H (2021) Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access 9:15561–15569

    Article  Google Scholar 

  • Zhou J, Jin P, Zhao J (2020) Sentiment analysis of online reviews with a hierarchical attention network. SEKE, pp 429–434

  • Zhu J, Wang H, Tsou BK, Zhu M (2009) Multi-aspect opinion polling from textual reviews. CIKM ‘09, pp 1799–1802

  • Zhu X, Zhu L, Guo J, Liang S, Dietze S (2021) GL-GCN: global and local dependency guided graph convolutional networks for aspect-based sentiment classification. Expert Syst Appl 186:115712

    Article  Google Scholar 

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  1. IRF-SIC Laboratory, Faculty of Science, Ibn Zohr University, Agadir, FP, Morocco

    Rajae Bensoltane & Taher Zaki

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  1. Rajae Bensoltane
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Bensoltane, R., Zaki, T. Aspect-based sentiment analysis: an overview in the use of Arabic language. Artif Intell Rev 56, 2325–2363 (2023). https://doi.org/10.1007/s10462-022-10215-3

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