International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 18, No. 5, 8 Oct. 2026
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Bibliometric Analysis of Aspect-based Sentiment Analysis Research: Trends, Challenges, and Future Directions
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Author(s)
Muhammad Resa Arif Yudianto
1,*
Handaru Jati
1
Fatchul Arifin
1
Index Terms
Aspect-Based Sentiment Analysis, Bibliometric Analysis, Opinion Mining, Sentiment Analysis Trends.
Abstract
Aspect-Based Sentiment Analysis (ABSA) has become an integral component of Natural Language Processing (NLP). It provides comprehensive insights into individuals' sentiments regarding specific aspects. This study conducts a comprehensive bibliometric analysis of 913 journal articles published from 2010 to 2024, sourced from the Scopus database, to examine trends, challenges, and prospective directions in ABSA research. The research examines the expansion of publications, citation metrics, and scholarly networks. It integrates performance analysis metrics (such as h-index, g-index, and citations per paper) with sophisticated science mapping techniques, including keyword co-occurrence networks, thematic evolution, and thematic mapping. This novel integrated approach, rarely employed in prior ABSA bibliometric studies, reveals both historical trends and emerging niche themes that remain underexplored. The findings illustrate the evolution of ABSA from rule-based methodologies to transformer-based architectures applicable in e-commerce, social media, and customer feedback systems. Key issues identified include multilingual adaptability, implicit sentiment detection, and cross-domain scalability. The research indicates that global institutions have significantly contributed, yet productivity and impact differ markedly across nations. Multimodal analysis, transfer learning, and contextualized models such as BERT are pivotal contemporary methodologies that can assist in addressing existing challenges. Future research should concentrate on integrating diverse disciplines, analyzing data across various languages and modalities, and developing scalable and comprehensible models. This study contributes to the domains of artificial intelligence and sentiment analysis by offering a comprehensive and data-driven overview of the ABSA landscape. It accomplishes this by providing both strategic insights and methodological enhancements.
Cite This Paper
Muhammad Resa Arif Yudianto, Handaru Jati, Fatchul Arifin, "Bibliometric Analysis of Aspect-based Sentiment Analysis Research: Trends, Challenges, and Future Directions", International Journal of Modern Education and Computer Science(IJMECS), Vol.18, No.5, pp. 68-86, 2026. DOI:10.5815/ijmecs.2026.05.04
Reference
[1]S. Ounacer, D. Mhamdi, S. Ardchir, A. Daif, and M. Azzouazi, “Customer Sentiment Analysis in Hotel Reviews Through Natural Language Processing Techniques,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 1, 2023. [Online]. Available: https://doi.org/10.14569/IJACSA.2023.0140162
[2]H. T. Ismet, T. Mustaqim, and D. Purwitasari, “Aspect Based Sentiment Analysis of Product Review Using Memory Network,” Scientific Journal of Informatics, vol. 9, no. 1, pp. 73–83, May 2022, doi: 10.15294/sji.v9i1.34094.
[3]C. B. P. Putra, D. Purwitasari, and A. B. Raharjo, “Stance Detection on Tweets with Multi-task Aspect-based Sentiment: A Case Study of COVID-19 Vaccination,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 5, pp. 515–526, Oct. 2022, doi: 10.22266/ijies2022.1031.45.
[4]D. Holubinka, V. Vysotska, S. Vladov, Y. Ushenko, M. Talakh, and Y. Tomka, “Intelligent System for Recognizing Tone and Categorizing Text in Media News at an Electronic Business Based on Sentiment and Sarcasm Analysis,” International Journal of Information Engineering and Electronic Business, vol. 17, no. 1, pp. 90–139, Feb. 2025.
[5]Y. Ma, M. He, Y. Pang, L. Wang, and H. Liu, “A Multi-feature Fusion Approach Based on Domain Adaptive Pretraining for Aspect-based Sentiment Analysis,” Soft Computing, vol. 28, no. 7–8, pp. 6101–6116, 2024, doi: 10.1007/s00500-023-09412-1.
[6]& M. A.-S. S. Al-Dabet S. Tedmori, “Extracting opinion targets using attention-based neural model,” SN Comput Sci, vol. 1, no. 5, 2020, [Online]. Available: https://doi.org/10.1007/s42979-020-00270-4
[7]M. Chen, “An interactive learning network that maintains sentiment consistency in end-to-end aspect-based sentiment analysis,” Applied Sciences, vol. 13, no. 16, p. 9327, 2023, [Online]. Available: https://doi.org/10.3390/app13169327
[8]A. Almasaud and H. Al-Baity, “Aramams: arabic multi-aspect, multi-sentiment restaurants reviews corpus for aspect-based sentiment analysis,” Sustainability, vol. 15, no. 16, p. 12268, 2023, [Online]. Available: https://doi.org/10.3390/su151612268
[9]S. Setyani, “Multi aspect sentiment analysis of mutual funds investment app bibit using bert method,” International Journal on Information and Communication Technology (Ijoict), vol. 9, no. 1, pp. 44–56, 2023, [Online]. Available: https://doi.org/10.21108/ijoict.v9i1.718
[10]S. Mohd Sofi and A. Selamat, “Aspect Based Sentiment Analysis: Feature Extraction using Latent Dirichlet Allocation (LDA) and Term Frequency-Inverse Document Frequency (TF-IDF) in Machine Learning (ML),” MyJICT-Malaysian Journal of Information and Communication Technology, vol. 8, no. 2, pp. 169–179, 2023, doi: 10.53840/myjict8-2-102.
[11]J. van de Ruitenbeek, F. Frasincar, and G. Brauwers, “A Memory-Driven Neural Attention Model for Aspect-Based Sentiment Classification,” Journal of Web Engineering, vol. 21, no. 6, pp. 1793–1830, 2022, doi: 10.13052/jwe1540-9589.2163.
[12]M. Syamala and N. Nalini, “ABSA: Computational Measurement Analysis Approach for Prognosticated Aspect Extraction System,” TEM Journal, vol. 10, no. 1, pp. 82–94, Feb. 2021, doi: 10.18421/TEM101-11.
[13]F. Gallego and R. Corchuelo, “Torii: an aspect‐based sentiment analysis system that can mine conditions,” Softw Pract Exp, vol. 50, no. 1, pp. 47–64, 2020, [Online]. Available: https://doi.org/10.1002/spe.2762
[14]N. Zhao, H. Gao, X. Wen, and H. Li, “Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis,” IEEE Access, vol. 9, pp. 15561–15569, 2021, doi: 10.1109/ACCESS.2021.3052937.
[15]H. Wicaksana, “Determining community happiness index with transformers and attention-based deep learning,” Iaes International Journal of Artificial Intelligence (Ij-Ai), vol. 13, no. 2, p. 1753, 2024, [Online]. Available: https://doi.org/10.11591/ijai.v13.i2.pp1753-1761
[16]H. M. M. E. & M. R. M. Hamza H. Alshahrani, “Battle royale optimization with fuzzy deep learning for arabic sentiment classification,” Computer Systems Science and Engineering, vol. 46, no. 2, pp. 2619–2635, 2023, [Online]. Available: https://doi.org/10.32604/csse.2023.034519
[17]N. Li C. Chow and J. Zhang, “Seml: a semi-supervised multi-task learning framework for aspect-based sentiment analysis,” Ieee Access, vol. 8, pp. 189287–189297, 2020, doi: 10.1109/access.2020.3031665.
[18]R. S. & C. F. S. Suhariyanto R. Abdullah, “Aspect-based sentiment analysis for sentence types with implicit aspect and explicit opinion in restaurant review using grammatical rules, hybrid approach, and senticircle,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 177–187, 2021, [Online]. Available: https://doi.org/10.22266/ijies2021.1031.17
[19]H. Park and J. Kim, “Stepwise multi-task learning model for holder extraction in aspect-based sentiment analysis,” Applied Sciences, vol. 12, no. 13, p. 6777, 2022, [Online]. Available: https://doi.org/10.3390/app12136777
[20]L. Hong, “Aspect-Based Sentiment Analysis of Specific Targets,” Highlights in Science, Engineering and Technology, vol. 85, pp. 443–451, 2024, doi: 10.54097/hdppsn74.
[21]Y. X. Y. H. & Z. Y. Y. Xiang J. Guo, “Hybrid node‐based tensor graph convolutional network for aspect‐category sentiment classification of microblog comments,” Concurr Comput, vol. 33, no. 21, 2021, [Online]. Available: https://doi.org/10.1002/cpe.6431
[22]S. Rida-E-Fatima et al., “A Multi-Layer Dual Attention Deep Learning Model with Refined Word Embeddings for Aspect-Based Sentiment Analysis,” IEEE Access, vol. 7, pp. 114795–114807, 2019, doi: 10.1109/ACCESS.2019.2927281.
[23]& R. S. R. Abdullah S. Suhariyanto, “Aspect based sentiment analysis for explicit and implicit aspects in restaurant review using grammatical rules, hybrid approach, and senticircle,” International Journal of Intelligent Engineering and Systems, vol. 14, no. 5, pp. 294–305, 2021, [Online]. Available: https://doi.org/10.22266/ijies2021.1031.27
[24]A. Firmanto and R. Sarno, “Aspect-based sentiment analysis using grammatical rules, word similarity and senticircle,” International Journal of Intelligent Engineering and Systems, vol. 12, no. 5, pp. 190–201, 2019, [Online]. Available: https://doi.org/10.22266/ijies2019.1031.19
[25]V. Thada and J. Singh, “A contemporary ensemble aspect-based opinion mining approach for twitter data,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 5, 2021, [Online]. Available: https://doi.org/10.14569/ijacsa.2021.0120524
[26]X. Zhan, C. Shi, L. Li, K. Xu, and H. Zheng, “Aspect category sentiment analysis based on multiple attention mechanisms and pre-trained models,” Applied and Computational Engineering, vol. 71, no. 1, pp. 21–26, May 2024, doi: 10.54254/2755-2721/71/2024ma0055.
[27]A. Bhowmik, N. M. Nur, M. S. U. Miah, and D. Karmaker, “Aspect-based Sentiment Analysis Model for Evaluating Teachers’ Performance from Students’ Feedback,” AIUB Journal of Science and Engineering (AJSE), vol. 22, no. 3, pp. 287–294, 2023, doi: 10.53799/ajse.v22i3.921.
[28]P. D. & M. R. T. Tran H. Hoan, “Toward a multitask aspect-based sentiment analysis model using deep learning,” Iaes International Journal of Artificial Intelligence (Ij-Ai), vol. 11, no. 2, p. 516, 2022, [Online]. Available: https://doi.org/10.11591/ijai.v11.i2.pp516-524
[29]& I. P. P. Aritonang M. Johan, “Aspect-based sentiment analysis on application review using convolutional neural network,” Ultima Infosys Jurnal Ilmu Sistem Informasi, vol. 13, no. 1, pp. 54–61, 2022, [Online]. Available: https://doi.org/10.31937/si.v13i1.2684
[30]A. Sowjanya, “Aspect-based sentiment analysis search engine for social media data,” Csi Transactions on Ict, vol. 8, no. 2, pp. 193–197, 2020, [Online]. Available: https://doi.org/10.1007/s40012-020-00295-3
[31]& S. K. S. Azarkasb M. Amiri, “Unsupervised fuzzy-multi-core aspect sentiment analysis for big data of online news users’ persian opinions,” Asian Journal of Research in Computer Science, vol. 16, no. 3, pp. 50–64, 2023, [Online]. Available: https://doi.org/10.9734/ajrcos/2023/v16i3345
[32]L. B. F. H. W. L. & L. S. H. Peng L. Xu, “Knowing what, how and why: a near complete solution for aspect-based sentiment analysis,” Proceedings of the Aaai Conference on Artificial Intelligence, vol. 34, no. 05, pp. 8600–8607, 2020, [Online]. Available: https://doi.org/10.1609/aaai.v34i05.6383
[33]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.
[34]Z. Jia, X. Bai, and S. Pang, “Hierarchical gated deep memory network with position-aware for aspect-based sentiment analysis,” IEEE Access, vol. 8, pp. 136340–136347, 2020, doi: 10.1109/ACCESS.2020.3011318.
[35]H. Huan, Z. He, Y. Xie, and Z. Guo, “A Multi-Task Dual-Encoder Framework for Aspect Sentiment Triplet Extraction,” IEEE Access, vol. 10, pp. 103187–103199, 2022, doi: 10.1109/ACCESS.2022.3210180.
[36]& G. C. N. Xu W. Mao, “Multi-interactive memory network for aspect based multimodal sentiment analysis,” Proceedings of the Aaai Conference on Artificial Intelligence, vol. 33, no. 01, pp. 371–378, 2019, [Online]. Available: https://doi.org/10.1609/aaai.v33i01.3301371
[37]G. Gomes U. Corrêa and L. Freitas, “Gabsa-pt: graph neural networks for aspect-level sentiment analysis in portuguese language,” The International Flairs Conference Proceedings, vol. 36, 2023, doi: 10.32473/flairs.36.133372.
[38]A. Rajan, “Aspect based sentiment analysis using fine-tuned bert model with deep context features,” Iaes International Journal of Artificial Intelligence (Ij-Ai), vol. 13, no. 2, p. 1250, 2024, [Online]. Available: https://doi.org/10.11591/ijai.v13.i2.pp1250-1261
[39]B. Bansal and S. Srivastava, “Aspect context aware sentiment classification of online consumer reviews,” Inf Discov Deliv, vol. 48, no. 3, pp. 117–128, 2020, [Online]. Available: https://doi.org/10.1108/idd-12-2019-0089
[40]P. N. Andono, Sunardi, R. A. Nugroho, and B. Harjo, “Aspect-Based Sentiment Analysis for Hotel Review Using LDA, Semantic Similarity, and BERT,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 5, pp. 232–243, Oct. 2022, doi: 10.22266/ijies2022.1031.21.
[41]& S. W. J. Huang Y. Cui, “Adaptive local context and syntactic feature modeling for aspect-based sentiment analysis,” Applied Sciences, vol. 13, no. 1, p. 603, 2023, [Online]. Available: https://doi.org/10.3390/app13010603
[42]S. Imron, “Aspect based sentiment analysis marketplace product reviews using bert, lstm, and cnn,” Jurnal Resti (Rekayasa Sistem Dan Teknologi Informasi), vol. 7, no. 3, pp. 586–591, 2023, [Online]. Available: https://doi.org/10.29207/resti.v7i3.4751
[43]A. wahhab and A. KareemabdulHassan, “Proposed aspect based sentiment analysis system for english reviews,” Journal of Al-Qadisiyah for Computer Science and Mathematics, vol. 11, no. 2, pp. 22–36, 2019, [Online]. Available: https://doi.org/10.29304/jqcm.2019.11.2.559
[44]Z. W. R. X. & H. T. Y. Qian C. Chen, “Sgpt: semantic graphs based pre-training for aspect-based sentiment analysis,” World Wide Web, vol. 26, no. 4, pp. 2201–2214, 2023, [Online]. Available: https://doi.org/10.1007/s11280-022-01123-1
[45]M. R. A. S. & S. H. D. Af’idah P. Anggraeni, “Aspect-based sentiment analysis for indonesian tourist attraction reviews using bidirectional long short-term memory,” Juita Jurnal Informatika, vol. 11, no. 1, p. 27, 2023, [Online]. Available: https://doi.org/10.30595/juita.v11i1.15341
[46]R. Ahmad Y. Shaikh and S. Tanwani, “Aspect based sentiment analysis and opinion mining on twitter data set using linguistic rules,” Indian J Sci Technol, vol. 16, no. 10, pp. 764–774, 2023, doi: 10.17485/ijst/v16i10.2138.
[47]S. Lestari and E. Setiawan, “Sentiment analysis based on aspects using fasttext feature expansion and nbsvm classification method,” Journal of Computer System and Informatics (Josyc), vol. 3, no. 4, pp. 469–477, 2022, [Online]. Available: https://doi.org/10.47065/josyc.v3i4.2202
[48]Y. Bie and Y. Yang, “A multitask multiview neural network for end-to-end aspect-based sentiment analysis,” Big Data Mining and Analytics, vol. 4, no. 3, pp. 195–207, 2021, [Online]. Available: https://doi.org/10.26599/bdma.2021.9020003
[49]A. Bavık and M. Köseoğlu, “Intellectual structure of leadership studies in hospitality and tourism,” International Journal of Contemporary Hospitality Management, vol. 34, no. 12, pp. 4562–4591, 2022, doi: 10.1108/ijchm-10-2021-1264.
[50]A. Singh and G. Pathak, “First three decades of cause-related marketing: building a deeper understanding through bibliometric analysis,” Journal of Management History, vol. 29, no. 4, pp. 582–607, 2023, [Online]. Available: https://doi.org/10.1108/jmh-11-2022-0067
[51]Y. L. X. C. S. J. H. Y. al. Y. Liu L. Yu, “Research landscape and trends of melanoma immunotherapy: a bibliometric analysis,” Front Oncol, vol. 12, 2023, doi: 10.3389/fonc.2022.1024179.
[52]N. Ellili, “Bibliometric analysis on corporate governance topics published in the journal of corporate governance: the international journal of business in society,” Corporate Governance, vol. 23, no. 1, pp. 262–286, 2023, [Online]. Available: https://doi.org/10.1108/cg-03-2022-0135
[53]N. Ellili, “Bibliometric analysis and systematic review of environmental, social, and governance disclosure papers: current topics and recommendations for future research,” Environ Res Commun, vol. 4, no. 9, p. 92001, 2022, [Online]. Available: https://doi.org/10.1088/2515-7620/ac8b67