Systematic Literature Review: Analisis Sentimen Berbasis Deep Learning

Fitroh Fitroh(1*), Fahmi Hudaya(2)
(1) Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta
(2) Program Studi Sistem Informasi, UIN Syarif Hidayatullah Jakarta
(*) Corresponding Author



Abstrak


Systematic literature review ini bertujuan untuk mengetahui tren penelitian analisis sentimen berbasis deep learning antara tahun 2020-2023. Fokus kajiannya adalah pada pemahaman tentang pemodelan yang digunakan oleh banyak peneliti, juga nilai akurasi dari masing-masing klasifikasi tersebut. Pertanyaan utama dalam SLR ini yaitu teknik analisis sentimen berbasis deep learning apa yang memberikan akurasi tertinggi. Peneliti menemukan 400 artikel terindeks Scopus dengan menggunakan Publish or Perish 8. Selanjutnya, penyaringan jurnal dan pencarian kluster menggunakan aplikasi Microsoft Excel, Zotero, Mendeley, dan VOS Viewer yang menghasilkan 105 artikel terpilih untuk dianalisis secara deskriptif. Berdasarkan hasil temuan metode yang populer digunakan dalam melakukan analisis sentimen berbasis deep learning dalam jangka waktu yang telah ditentukan adalah metode LSTM dan CNN, baik dilakukan satu metode maupun keduanya. Adapun akurasi tertinggi mencapai 99% dengan rata-rata 89% menggunakan metode LSTM. Pengetahuan ini dapat digunakan untuk mengusulkan model analisis sentimen berbasis deep learning yang memberikan akurasi tertinggi.


Kata Kunci


Systematic Literature Review; Analisis Sentimen; Deep Learning


Teks Lengkap:

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Referensi


[1] S. Kemp, Digital 2023 April Global Statshot Report, Datareportal, Apr. 27, 2023. https://datareportal.com/reports/digital-2023-april-global-statshot (accessed May 20, 2023).

[2] N. C. Dang, M. N. Moreno-Garc a, and F. De la Prieta, Sentiment Analysis Based on Deep Learning: A Comparative Study, Electronics (Basel), vol. 9, no. 3, p. 483, Mar. 2020, doi: 10.3390/electronics9030483.

[3] A. Yadav and D. K. Vishwakarma, Sentiment analysis using deep learning architectures: a review, Artif Intell Rev, vol. 53, no. 6, pp. 4335 4385, 2020, doi: 10.1007/s10462-019-09794-5.

[4] J. P rez, J. D az, J. Garcia-Martin, and B. Tabuenca, Systematic literature reviews in software engineering enhancement of the study selection process using Cohen s Kappa statistic, Journal of Systems and Software, vol. 168, p. 110657, Oct. 2020, doi: 10.1016/j.jss.2020.110657.

[5] G. Matturro, F. Raschetti, and C. Font n, A Systematic Mapping Study on Soft Skills in Software Engineering, JUCS - Journal of Universal Computer Science, vol. 25, no. 1, pp. 16 41, 2019, doi: 10.3217/jucs-025-01-0016.

[6] B. Kitchenham and S. M. Charters, Guidelines for performing Systematic Literature Reviews in Software Engineering, vol. 2, Jan. 2007, Accessed: May 08, 2023. [Online]. Available: https://www.researchgate.net/publication/302924724_Guidelines_for_performing_Systematic_Literature_Reviews_in_Software_Engineering

[7] B. Kitchenham et al., Systematic literature reviews in software engineering A tertiary study, Inf Softw Technol, vol. 52, no. 8, pp. 792 805, Aug. 2010, doi: 10.1016/j.infsof.2010.03.006.

[8] A. S. Imran, S. M. Daudpota, Z. Kastrati, and R. Batra, Cross-cultural polarity and emotion detection using sentiment analysis and deep learning on covid-19 related tweets, IEEE Access, vol. 8, pp. 181074 181090, 2020, doi: 10.1109/ACCESS.2020.3027350.

[9] A. Onan, Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach, Computer Applications in Engineering Education, vol. 29, no. 3, pp. 572 589, May 2021, doi: 10.1002/cae.22253.

[10] S. Kardakis, I. Perikos, F. Grivokostopoulou, and I. Hatzilygeroudis, Examining attention mechanisms in deep learning models for sentiment analysis, Applied Sciences (Switzerland), vol. 11, no. 9, 2021, doi: 10.3390/app11093883.

[11] M. Parimala, R. M. Swarna Priya, M. Praveen Kumar Reddy, C. Lal Chowdhary, R. Kumar Poluru, and S. Khan, Spatiotemporal-based sentiment analysis on tweets for risk assessment of event using deep learning approach, Softw Pract Exp, vol. 51, no. 3, pp. 550 570, 2021, doi: 10.1002/spe.2851.

[12] F. Huang, K. Wei, J. Weng, and Z. Li, Attention-Based Modality-Gated Networks for Image-Text Sentiment Analysis, ACM Transactions on Multimedia Computing, Communications and Applications, vol. 16, no. 3, 2020, doi: 10.1145/3388861.

[13] A. R. Pathak, M. Pandey, and S. Rautaray, Topic-level sentiment analysis of social media data using deep learning, Appl Soft Comput, vol. 108, 2021, doi: 10.1016/j.asoc.2021.107440.

[14] U. D. Gandhi, P. Malarvizhi Kumar, G. Chandra Babu, and G. Karthick, Sentiment Analysis on Twitter Data by Using Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), Wirel Pers Commun, 2021, doi: 10.1007/s11277-021-08580-3.

[15] S. Vernikou, A. Lyras, and A. Kanavos, Multiclass sentiment analysis on COVID-19-related tweets using deep learning models, Neural Comput Appl, vol. 34, no. 22, pp. 19615 19627, 2022, doi: 10.1007/s00521-022-07650-2.

[16] P. Chakraborty, F. Nawar, and H. A. Chowdhury, Sentiment Analysis of Bengali Facebook Data Using Classical and Deep Learning Approaches, Lecture Notes in Electrical Engineering, vol. 814, pp. 209 218, 2022, doi: 10.1007/978-981-16-7076-3_19.

[17] H. Swapnarekha, J. Nayak, H. S. Behera, P. B. Dash, and D. Pelusi, An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets, Mathematical Biosciences and Engineering, vol. 20, no. 2, pp. 2382 2407, 2023, doi: 10.3934/mbe.2023112.

[18] T. M. Omran, B. T. Sharef, C. Grosan, and Y. Li, Transfer learning and sentiment analysis of Bahraini dialects sequential text data using multilingual deep learning approach, Data Knowl Eng, vol. 143, 2023, doi: 10.1016/j.datak.2022.102106.

[19] Y. Abdelwahab, M. Kholief, and A. A. H. Sedky, Justifying Arabic Text Sentiment Analysis Using Explainable AI (XAI): LASIK Surgeries Case Study, Information (Switzerland), vol. 13, no. 11, 2022, doi: 10.3390/info13110536.

[20] A. Chamekh, M. Mahfoudh, and G. Forestier, Sentiment Analysis Based on Deep Learning in E-Commerce, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13369 LNAI, pp. 498 507, 2022, doi: 10.1007/978-3-031-10986-7_40.

[21] H. Sankar, V. Subramaniyaswamy, V. Vijayakumar, S. Arun Kumar, R. Logesh, and A. Umamakeswari, Intelligent sentiment analysis approach using edge computing-based deep learning technique, Softw Pract Exp, vol. 50, no. 5, pp. 645 657, 2020, doi: 10.1002/spe.2687.

[22] B. Liu, Text sentiment analysis based on CBOW model and deep learning in big data environment, J Ambient Intell Humaniz Comput, vol. 11, no. 2, pp. 451 458, Feb. 2020, doi: 10.1007/s12652-018-1095-6.

[23] C. Sitaula, A. Basnet, A. Mainali, and T. B. Shahi, Deep Learning-Based Methods for Sentiment Analysis on Nepali COVID-19-Related Tweets, Comput Intell Neurosci, vol. 2021, pp. 1 11, Nov. 2021, doi: 10.1155/2021/2158184.

[24] K. Dashtipour, M. Gogate, J. Li, F. Jiang, B. Kong, and A. Hussain, A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks, Neurocomputing, vol. 380, pp. 1 10, 2020, doi: 10.1016/j.neucom.2019.10.009.

[25] J. Yang and J. Yang, Aspect Based Sentiment Analysis with Self-Attention and Gated Convolutional Networks, Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, vol. 2020-Octob, pp. 146 149, 2020, doi: 10.1109/ICSESS49938.2020.9237640.

[26] O. J. Ying, M. M. A. Zabidi, N. Ramli, and U. U. Sheikh, Sentiment analysis of informal malay tweets with deep learning, IAES International Journal of Artificial Intelligence, vol. 9, no. 2, pp. 212 220, 2020, doi: 10.11591/ijai.v9.i2.pp212-220.

[27] H. Li, Y. Ma, Z. Ma, and H. Zhu, Weibo text sentiment analysis based on bert and deep learning, Applied Sciences (Switzerland), vol. 11, no. 22, 2021, doi: 10.3390/app112210774.

[28] A. Alqarni and A. Rahman, Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach, Big Data and Cognitive Computing, vol. 7, no. 1, 2023, doi: 10.3390/bdcc7010016.

[29] A. Ghorbanali, M. K. Sohrabi, and F. Yaghmaee, Ensemble transfer learning-based multimodal sentiment analysis using weighted convolutional neural networks, Inf Process Manag, vol. 59, no. 3, 2022, doi: 10.1016/j.ipm.2022.102929.

[30] I. Salehin et al., Analysis of student sentiment during video class with multilayer deep learning approach, International Journal of Electrical and Computer Engineering, vol. 12, no. 4, pp. 3981 3993, 2022, doi: 10.11591/ijece.v12i4.pp3981-3993.

[31] R. Alatrash, R. Priyadarshini, H. Ezaldeen, and A. Alhinnawi, Augmented language model with deep learning adaptation on sentiment analysis for E-learning recommendation, Cogn Syst Res, vol. 75, pp. 53 69, 2022, doi: 10.1016/j.cogsys.2022.07.002.

[32] D. I. N. Afra et al., Developing Sentiment Analysis of Indonesian Social Media Based on Convolutional Neural Network for Smarter Society, 9th International Conference on ICT for Smart Society: Recover Together, Recover Stronger and Smarter Smartization, Governance and Collaboration, ICISS 2022 - Proceeding, 2022, doi: 10.1109/ICISS55894.2022.9915148.

[33] A. Onan, Sentiment analysis on product reviews based on weighted word embeddings and deep neural networks, Concurr Comput, vol. 33, no. 23, Dec. 2021, doi: 10.1002/cpe.5909.

[34] C. N. Dang, M. N. Moreno-Garc a, and F. De la Prieta, An approach to integrating sentiment analysis into recommender systems, Sensors, vol. 21, no. 16, 2021, doi: 10.3390/s21165666.

[35] M. Ghorbani, M. Bahaghighat, Q. Xin, and F. zen, ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing, Journal of Cloud Computing, vol. 9, no. 1, p. 16, Dec. 2020, doi: 10.1186/s13677-020-00162-1.

[36] C. N. Dang, M. N. Moreno-Garc a, and F. De La Prieta, Hybrid Deep Learning Models for Sentiment Analysis, Complexity, vol. 2021, 2021, doi: 10.1155/2021/9986920.

[37] P. K. Jain, V. Saravanan, and R. Pamula, A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 20, no. 5, 2021, doi: 10.1145/3457206.

[38] A. H. Ombabi, W. Ouarda, and A. M. Alimi, Deep learning CNN LSTM framework for Arabic sentiment analysis using textual information shared in social networks, Soc Netw Anal Min, vol. 10, no. 1, p. 53, Dec. 2020, doi: 10.1007/s13278-020-00668-1.

[39] V. Tyagi, A. Kumar, and S. Das, Sentiment Analysis on Twitter Data Using Deep Learning approach, Proceedings - IEEE 2020 2nd International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020, pp. 187 190, 2020, doi: 10.1109/ICACCCN51052.2020.9362853.

[40] N. Hossain, Md. R. Bhuiyan, Z. N. Tumpa, and S. A. Hossain, Sentiment Analysis of Restaurant Reviews using Combined CNN-LSTM, in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IEEE, Jul. 2020, pp. 1 5. doi: 10.1109/ICCCNT49239.2020.9225328.

[41] A. Alwehaibi, M. Bikdash, M. Albogmi, and K. Roy, A study of the performance of embedding methods for Arabic short-text sentiment analysis using deep learning approaches, Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 8, pp. 6140 6149, 2022, doi: 10.1016/j.jksuci.2021.07.011.

[42] F. Correia, A. M. Madureira, and J. Bernardino, Deep Neural Networks Applied to Stock Market Sentiment Analysis, Sensors, vol. 22, no. 12, 2022, doi: 10.3390/s22124409.

[43] H. Peng, Z. Zhang, and H. Liu, A Sentiment Analysis Method for Teaching Evaluation Texts Using Attention Mechanism Combined with CNN-BLSTM Model, Sci Program, vol. 2022, 2022, doi: 10.1155/2022/8496151.

[44] A. Chiorrini, C. Diamantini, A. Mircoli, and D. Potena, Emotion and sentiment analysis of tweets using BERT, 2021. [Online]. Available: https://code.google.com/archive/p/word2vec/

[45] M. Boukabous and M. Azizi, Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers, Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 2, pp. 1131 1139, 2022, doi: 10.11591/ijeecs.v25.i2.pp1131-1139.

[46] I. Aygun, B. Kaya, and M. Kaya, Aspect Based Twitter Sentiment Analysis on Vaccination and Vaccine Types in COVID-19 Pandemic With Deep Learning, IEEE J Biomed Health Inform, vol. 26, no. 5, pp. 2360 2369, 2022, doi: 10.1109/JBHI.2021.3133103.

[47] L. Mai and B. Le, Joint sentence and aspect-level sentiment analysis of product comments, Ann Oper Res, vol. 300, no. 2, pp. 493 513, May 2021, doi: 10.1007/s10479-020-03534-7.

[48] L. Zhao, Y. Liu, M. Zhang, T. Guo, and L. Chen, Modeling label-wise syntax for fine-grained sentiment analysis of reviews via memory-based neural model, Inf Process Manag, vol. 58, no. 5, p. 102641, Sep. 2021, doi: 10.1016/j.ipm.2021.102641.

[49] M. J. Althobaiti, BERT-based Approach to Arabic Hate Speech and Offensive Language Detection in Twitter: Exploiting Emojis and Sentiment Analysis, International Journal of Advanced Computer Science and Applications, vol. 13, no. 5, pp. 972 980, 2022, doi: 10.14569/IJACSA.2022.01305109.

[50] A. B. Nassif, A. M. Darya, and A. Elnagar, Empirical Evaluation of Shallow and Deep Learning Classifiers for Arabic Sentiment Analysis, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 1, 2022, doi: 10.1145/3466171.

[51] P. K. Jain, W. Quamer, R. Pamula, and V. Saravanan, SpSAN: Sparse self-attentive network-based aspect-aware model for sentiment analysis, J Ambient Intell Humaniz Comput, vol. 14, no. 4, pp. 3091 3108, 2021, doi: 10.1007/s12652-021-03436-x.

[52] Mamta, A. Ekbal, P. Bhattacharyya, T. Saha, A. Kumar, and S. Srivastava, HindiMD: A Multi-domain Corpora for Low-resource Sentiment Analysis, 2022 Language Resources and Evaluation Conference, LREC 2022, pp. 7061 7070, 2022.

[53] A. Kumar, K. Srinivasan, W. H. Cheng, and A. Y. Zomaya, Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data, Inf Process Manag, vol. 57, no. 1, 2020, doi: 10.1016/j.ipm.2019.102141.

[54] K. Chakraborty, S. Bhatia, S. Bhattacharyya, J. Platos, R. Bag, and A. E. Hassanien, Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers A study to show how popularity is affecting accuracy in social media, Applied Soft Computing Journal, vol. 97, 2020, doi: 10.1016/j.asoc.2020.106754.

[55] G. M. Raza, Z. S. Butt, S. Latif, and A. Wahid, Sentiment Analysis on COVID Tweets: An Experimental Analysis on the Impact of Count Vectorizer and TF-IDF on Sentiment Predictions using Deep Learning Models, 2021 International Conference on Digital Futures and Transformative Technologies, ICoDT2 2021, 2021, doi: 10.1109/ICoDT252288.2021.9441508.

[56] K. Ahmed et al., Exploiting Stacked Autoencoders for Improved Sentiment Analysis, Applied Sciences (Switzerland), vol. 12, no. 23, 2022, doi: 10.3390/app122312380.

[57] S. Maity and K. Sarkar, Topic Sentiment Analysis for Twitter Data in Indian Languages Using Composite Kernel SVM and Deep Learning, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 5, pp. 1 35, 2022, doi: 10.1145/3519297.

[58] L. C. Chen, C. M. Lee, and M. Y. Chen, Exploration of social media for sentiment analysis using deep learning, Soft comput, vol. 24, no. 11, pp. 8187 8197, 2020, doi: 10.1007/s00500-019-04402-8.

[59] K. N. Alam et al., Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter Data, Comput Math Methods Med, vol. 2021, 2021, doi: 10.1155/2021/4321131.

[60] W. L. Lim, C. C. Ho, and C. Y. Ting, Sentiment analysis by fusing text and location features of geo-tagged tweets, IEEE Access, vol. 8, pp. 181014 181027, 2020, doi: 10.1109/ACCESS.2020.3027845.

[61] B. A. Chandio, A. S. Imran, M. Bakhtyar, S. M. Daudpota, and J. Baber, Attention-Based RU-BiLSTM Sentiment Analysis Model for Roman Urdu, Applied Sciences (Switzerland), vol. 12, no. 7, 2022, doi: 10.3390/app12073641.

[62] L. Wang, X. Xu, C. Liu, and Z. Chen, M-DA: A Multifeature Text Data-Augmentation Model for Improving Accuracy of Chinese Sentiment Analysis, Sci Program, vol. 2022, 2022, doi: 10.1155/2022/3264378.

[63] M. E. Basiri, S. Nemati, M. Abdar, E. Cambria, and U. R. Acharya, ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis, Future Generation Computer Systems, vol. 115, pp. 279 294, 2021, doi: 10.1016/j.future.2020.08.005.

[64] H. Sadr, M. M. Pedram, and M. Teshnehlab, Multi-View Deep Network: A Deep Model Based on Learning Features from Heterogeneous Neural Networks for Sentiment Analysis, IEEE Access, vol. 8, pp. 86984 86997, 2020, doi: 10.1109/ACCESS.2020.2992063.

[65] M. Beseiso and H. Elmousalami, Subword Attentive Model for Arabic Sentiment Analysis, ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 19, no. 2, pp. 1 17, Mar. 2020, doi: 10.1145/3360016.

[66] M. Kamyab, G. Liu, A. Rasool, and M. Adjeisah, ACR-SA: attention-based deep model through two-channel CNN and Bi-RNN for sentiment analysis, PeerJ Comput Sci, vol. 8, 2022, doi: 10.7717/peerj-cs.877.

[67] C. Iwendi, S. Mohan, S. khan, E. Ibeke, A. Ahmadian, and T. Ciano, Covid-19 fake news sentiment analysis, Computers and Electrical Engineering, vol. 101, 2022, doi: 10.1016/j.compeleceng.2022.107967.

[68] M. M.Abdelgwad, T. H. A Soliman, A. I.Taloba, and M. F. Farghaly, Arabic aspect based sentiment analysis using bidirectional GRU based models, Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 9, pp. 6652 6662, Oct. 2022, doi: 10.1016/j.jksuci.2021.08.030.

[69] M. Zulqarnain, R. Ghazali, M. Aamir, and Y. M. M. Hassim, An efficient two-state GRU based on feature attention mechanism for sentiment analysis, Multimed Tools Appl, 2022, doi: 10.1007/s11042-022-13339-4.

[70] M. R. Raza, W. Hussain, and A. Varol, Performance Analysis of Deep Approaches on Airbnb Sentiment Reviews, in 2022 10th International Symposium on Digital Forensics and Security (ISDFS), IEEE, Jun. 2022, pp. 1 5. doi: 10.1109/ISDFS55398.2022.9800816.

[71] S. Kula, M. Choraś, R. Kozik, P. Ksieniewicz, and M. Woźniak, Sentiment analysis for fake news detection by means of neural networks, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12140 LNCS, pp. 653 666, 2020, doi: 10.1007/978-3-030-50423-6_49.

[72] M. H. Munna, M. R. I. Rifat, and A. S. M. Badrudduza, Sentiment Analysis and Product Review Classification in E-commerce Platform, ICCIT 2020 - 23rd International Conference on Computer and Information Technology, Proceedings, 2020, doi: 10.1109/ICCIT51783.2020.9392710.

[73] R. K. Mishra, S. Urolagin, J. A. A. Jothi, A. S. Neogi, and N. Nawaz, Deep Learning-based Sentiment Analysis and Topic Modeling on Tourism During Covid-19 Pandemic, Front Comput Sci, vol. 3, Nov. 2021, doi: 10.3389/fcomp.2021.775368.

[74] N. M. Alharbi, N. S. Alghamdi, E. H. Alkhammash, and J. F. Al Amri, Evaluation of Sentiment Analysis via Word Embedding and RNN Variants for Amazon Online Reviews, Math Probl Eng, vol. 2021, 2021, doi: 10.1155/2021/5536560.

[75] I. Safder et al., Sentiment analysis for Urdu online reviews using deep learning models, Expert Syst, vol. 38, no. 8, Dec. 2021, doi: 10.1111/exsy.12751.

[76] A. A. Nagra, K. Alissa, T. M. Ghazal, S. Saigeeta, M. M. Asif, and M. Fawad, Deep Sentiments Analysis for Roman Urdu Dataset Using Faster Recurrent Convolutional Neural Network Model, Applied Artificial Intelligence, vol. 36, no. 1, 2022, doi: 10.1080/08839514.2022.2123094.

[77] M. Kamyab, G. Liu, and M. Adjeisah, Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis, Applied Sciences (Switzerland), vol. 11, no. 23, 2021, doi: 10.3390/app112311255.

[78] V. Kumar, Spatiotemporal sentiment variation analysis of geotagged COVID-19 tweets from India using a hybrid deep learning model, Sci Rep, vol. 12, no. 1, 2022, doi: 10.1038/s41598-022-05974-6.

[79] G. Chandrasekaran, N. Antoanela, G. Andrei, C. Monica, and J. Hemanth, Visual Sentiment Analysis Using Deep Learning Models with Social Media Data, Applied Sciences (Switzerland), vol. 12, no. 3, 2022, doi: 10.3390/app12031030.

[80] K. L. Tan, C. P. Lee, K. S. M. Anbananthen, and K. M. Lim, RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network, IEEE Access, vol. 10, pp. 21517 21525, 2022, doi: 10.1109/ACCESS.2022.3152828.

[81] S. B. Garg and V. V. Subrahmanyam, Sentiment Analysis: Choosing the Right Word Embedding for Deep Learning Model, Lecture Notes in Networks and Systems, vol. 218, pp. 417 428, 2022, doi: 10.1007/978-981-16-2164-2_33.

[82] S. Behl, A. Rao, S. Aggarwal, S. Chadha, and H. S. Pannu, Twitter for disaster relief through sentiment analysis for COVID-19 and natural hazard crises, International Journal of Disaster Risk Reduction, vol. 55, 2021, doi: 10.1016/j.ijdrr.2021.102101.

[83] Z. Lyu and H. Takikawa, Media framing and expression of anti-China sentiment in COVID-19-related news discourse: An analysis using deep learning methods, Heliyon, vol. 8, no. 8, 2022, doi: 10.1016/j.heliyon.2022.e10419.

[84] W. Liao, B. Zeng, J. Liu, P. Wei, X. Cheng, and W. Zhang, Multi-level graph neural network for text sentiment analysis, Computers and Electrical Engineering, vol. 92, 2021, doi: 10.1016/j.compeleceng.2021.107096.

[85] N. A. Alkhaldi, Y. Asiri, A. M. Mashraqi, H. T. Halawani, S. Abdel‐khalek, and R. F. Mansour, Leveraging Tweets for Artificial Intelligence Driven Sentiment Analysis on the COVID‐19 Pandemic, Healthcare (Switzerland), vol. 10, no. 5, 2022, doi: 10.3390/healthcare10050910.

[86] S. Al-Dabet, S. Tedmori, and M. AL-Smadi, Enhancing Arabic aspect-based sentiment analysis using deep learning models, Comput Speech Lang, vol. 69, 2021, doi: 10.1016/j.csl.2021.101224.

[87] M. A. El-Affendi, K. Alrajhi, and A. Hussain, A Novel Deep Learning-Based Multilevel Parallel Attention Neural (MPAN) Model for Multidomain Arabic Sentiment Analysis, IEEE Access, vol. 9, pp. 7508 7518, 2021, doi: 10.1109/ACCESS.2021.3049626.

[88] N. Liu and B. Shen, ReMemNN: A novel memory neural network for powerful interaction in aspect-based sentiment analysis, Neurocomputing, vol. 395, pp. 66 77, 2020, doi: 10.1016/j.neucom.2020.02.018.

[89] S. Sachin, A. Tripathi, N. Mahajan, S. Aggarwal, and P. Nagrath, Sentiment Analysis Using Gated Recurrent Neural Networks, SN Comput Sci, vol. 1, no. 2, 2020, doi: 10.1007/s42979-020-0076-y.

[90] R. Ni and H. Cao, Sentiment Analysis based on GloVe and LSTM-GRU, Chinese Control Conference, CCC, vol. 2020-July, pp. 7492 7497, 2020, doi: 10.23919/CCC50068.2020.9188578.

[91] L. Kurniasari and A. Setyanto, Sentiment Analysis using Recurrent Neural Network, J Phys Conf Ser, vol. 1471, no. 1, 2020, doi: 10.1088/1742-6596/1471/1/012018.

[92] T. Swathi, N. Kasiviswanath, and A. A. Rao, An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis, Applied Intelligence, vol. 52, no. 12, pp. 13675 13688, 2022, doi: 10.1007/s10489-022-03175-2.

[93] J. Zhou, J. X. Huang, Q. V. Hu, and L. He, Is position important? deep multi-task learning for aspect-based sentiment analysis, Applied Intelligence, vol. 50, no. 10, pp. 3367 3378, 2020, doi: 10.1007/s10489-020-01760-x.

[94] N. Habbat, H. Anoun, and L. Hassouni, Combination of GRU and CNN deep learning models for sentiment analysis on French customer reviews using XLNet model, IEEE Engineering Management Review, vol. 51, no. 1, pp. 41 51, 2022, doi: 10.1109/EMR.2022.3208818.

[95] M. B. Dastgheib, S. Koleini, and F. Rasti, The application of deep learning in Persian documents sentiment analysis, International Journal of Information Science and Management, vol. 18, no. 1, pp. 1 15, 2020.

[96] A. Issam, A. K. Mounir, E. M. Saida, and E. M. Fatna, Financial sentiment analysis of tweets based on deep learning approach, Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1759 1770, 2022, doi: 10.11591/ijeecs.v25.i3.pp1759-1770.

[97] B. Albadani, R. Shi, J. Dong, R. Al-Sabri, and O. B. Moctard, Transformer-Based Graph Convolutional Network for Sentiment Analysis, Applied Sciences (Switzerland), vol. 12, no. 3, 2022, doi: 10.3390/app12031316.

[98] A. Khan, Improved multi-lingual sentiment analysis and recognition using deep learning, J Inf Sci, 2023, doi: 10.1177/01655515221137270.

[99] K. L. Tan, C. P. Lee, K. M. Lim, and K. S. M. Anbananthen, Sentiment Analysis With Ensemble Hybrid Deep Learning Model, IEEE Access, vol. 10, pp. 103694 103704, 2022, doi: 10.1109/ACCESS.2022.3210182.

[100] D. Sunitha, R. K. Patra, N. V. Babu, A. Suresh, and S. C. Gupta, Twitter sentiment analysis using ensemble based deep learning model towards COVID-19 in India and European countries, Pattern Recognit Lett, vol. 158, pp. 164 170, 2022, doi: 10.1016/j.patrec.2022.04.027.

[101] S. N. Zisad, E. Chowdhury, M. S. Hossain, R. U. Islam, and K. Andersson, An integrated deep learning and belief rule-based expert system for visual sentiment analysis under uncertainty, Algorithms, vol. 14, no. 7, 2021, doi: 10.3390/a14070213.

[102] J. Shobana and M. Murali, An efficient sentiment analysis methodology based on long short-term memory networks, Complex and Intelligent Systems, vol. 7, no. 5, pp. 2485 2501, 2021, doi: 10.1007/s40747-021-00436-4.

[103] U. Naqvi, A. Majid, and S. A. Abbas, UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods, IEEE Access, vol. 9, pp. 114085 114094, 2021, doi: 10.1109/ACCESS.2021.3104308.

[104] F. Huang, X. Li, C. Yuan, S. Zhang, J. Zhang, and S. Qiao, Attention-Emotion-Enhanced Convolutional LSTM for Sentiment Analysis, IEEE Trans Neural Netw Learn Syst, 2021, doi: 10.1109/TNNLS.2021.3056664.

[105] T. U. Tran, H. T. T. Hoang, and H. X. Huynh, Bidirectional Independently Long Short-Term Memory and Conditional Random Field Integrated Model for Aspect Extraction in Sentiment Analysis, Advances in Intelligent Systems and Computing, vol. 1014, pp. 131 140, 2020, doi: 10.1007/978-981-13-9920-6_14.

[106] N. Liu and B. Shen, Aspect-based sentiment analysis with gated alternate neural network, Knowl Based Syst, vol. 188, 2020, doi: 10.1016/j.knosys.2019.105010.

[107] Y. Liu, J. Lu, J. Yang, and F. Mao, Sentiment analysis for e-commerce product reviews by deep learning model of Bert-BiGRU-Softmax, Mathematical Biosciences and Engineering, vol. 17, no. 6, pp. 7819 7837, 2020, doi: 10.3934/MBE.2020398.

[108] Z. H. Kilimci, Sentiment Analysis Based Direction Prediction in Bitcoin using Deep Learning Algorithms and Word Embedding Models, International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 2, pp. 60 65, Jun. 2020, doi: 10.18201/ijisae.2020261585.

[109] H. Kaur, S. U. Ahsaan, B. Alankar, and V. Chang, A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets, Information Systems Frontiers, vol. 23, no. 6, pp. 1417 1429, 2021, doi: 10.1007/s10796-021-10135-7.

[110] K. M. Hasib, M. A. Habib, N. A. Towhid, and M. I. H. Showrov, A Novel Deep Learning based Sentiment Analysis of Twitter Data for US Airline Service, 2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021 - Proceedings, pp. 450 455, 2021, doi: 10.1109/ICICT4SD50815.2021.9396879.

[111] J. Su, S. Yu, and D. Luo, Enhancing Aspect-Based Sentiment Analysis with Capsule Network, IEEE Access, vol. 8, pp. 100551 100561, 2020, doi: 10.1109/ACCESS.2020.2997675.

[112] P. Demotte, K. Wijegunarathna, D. Meedeniya, and I. Perera, Enhanced sentiment extraction architecture for social media content analysis using capsule networks, Multimed Tools Appl, vol. 82, no. 6, pp. 8665 8690, 2023, doi: 10.1007/s11042-021-11471-1.


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