{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:26:39Z","timestamp":1774034799455,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Product reviews on the marketplace are interesting to research. Aspect-based sentiment analysis (ABSA) can be used to find in-depth information from a review. In one review, there can be several aspects with a polarity of sentiment. Previous research has developed ABSA, but it still has limitations in detecting aspects and sentiment classification and requires labeled data, but obtaining labeled data is very difficult. This research used a graph-based and semi-supervised approach to improve ABSA. GCN and GRN methods are used to detect aspect and opinion relationships. CNN and RNN methods are used to improve sentiment classification. A semi-supervised model was used to overcome the limitations of labeled data. The dataset used is an Indonesian-language review taken from the marketplace. A small part is labeled manually, and most are labeled automatically. The experiment results for the aspect classification by comparing the GCN and GRN methods obtained the best model using the GRN method with an F1 score = 0.97144. The experiment for sentiment classification by comparing the CNN and RNN methods obtained the best model using the CNN method with an F1 score = 0.94020. Our model can label most unlabeled data automatically and outperforms existing advanced models.<\/jats:p>","DOI":"10.3390\/bdcc7010005","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T02:56:50Z","timestamp":1672282610000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Graph-Based Semi-Supervised Deep Learning for Indonesian Aspect-Based Sentiment Analysis"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7951-639X","authenticated-orcid":false,"given":"Ahmad Abdul","family":"Chamid","sequence":"first","affiliation":[{"name":"Doctoral Program in Information Systems School of Postgraduate Studies, Diponegoro University, Semarang 50275, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4372-6501","authenticated-orcid":false,"family":"Widowati","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Science and Mathematics, Diponegoro University, Semarang 50275, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3606-436X","authenticated-orcid":false,"given":"Retno","family":"Kusumaningrum","sequence":"additional","affiliation":[{"name":"Department of Informatics, Faculty of Science and Mathematics, Diponegoro University, Semarang 50275, Indonesia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yeasmin, N., Mahbub, N.I., Baowaly, M.K., Singh, B.C., Alom, Z., Aung, Z., and Azim, M.A. (2022). Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020065"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Al Shamsi, A.A., and Abdallah, S. (2022). Sentiment Analysis of Emirati Dialects. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020057"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Khabour, S.M., Al-Radaideh, Q.A., and Mustafa, D. (2022). A New Ontology-Based Method for Arabic Sentiment Analysis. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020048"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.procs.2022.09.327","article-title":"Evaluation of weakly-supervised methods for aspect extraction","volume":"207","author":"Ettaleb","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"108668","DOI":"10.1016\/j.knosys.2022.108668","article-title":"An enhanced guided LDA model augmented with BERT based semantic strength for aspect term extraction in sentiment analysis","volume":"246","author":"Venugopalan","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1016\/j.neucom.2022.07.067","article-title":"Dependency graph enhanced interactive attention network for aspect sentiment triplet extraction","volume":"507","author":"Shi","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109954","DOI":"10.1016\/j.knosys.2022.109954","article-title":"Sector-level sentiment analysis with deep learning","volume":"258","author":"Almalis","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107058","DOI":"10.1016\/j.knosys.2021.107058","article-title":"Comparison of neutrosophic approach to various deep learning models for sentiment analysis","volume":"223","author":"Sharma","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yu, H., Lu, G., Cai, Q., and Xue, Y. (2022). A KGE Based Knowledge Enhancing Method for Aspect-Level Sentiment Classification. Mathematics, 10.","DOI":"10.3390\/math10203908"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"101224","DOI":"10.1016\/j.csl.2021.101224","article-title":"Enhancing Arabic aspect-based sentiment analysis using deep learning models","volume":"69","author":"Tedmori","year":"2021","journal-title":"Comput. Speech Lang."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ismail, H., Khalil, A., Hussein, N., and Elabyad, R. (2022). Triggers and Tweets: Implicit Aspect-Based Sentiment and Emotion Analysis of Community Chatter Relevant to Education Post-COVID-19. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6030099"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cendani, L.M., Kusumaningrum, R., and Endah, S.N. (2023). Aspect-Based Sentiment Analysis of Indonesian-Language Hotel Reviews Using Long Short-Term Memory with an Attention Mechanism, Springer International Publishing.","DOI":"10.1007\/978-3-031-15191-0_11"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"109409","DOI":"10.1016\/j.knosys.2022.109409","article-title":"Attention-based aspect sentiment classification using enhanced learning through CNN-BiLSTM networks","volume":"252","author":"Ayetiran","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108964","DOI":"10.1016\/j.patcog.2022.108964","article-title":"Enhancement of DNN-based multilabel classification by grouping labels based on data imbalance and label correlation","volume":"132","author":"Chen","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jasmir, J., Nurmaini, S., and Tutuko, B. (2021). Fine-grained algorithm for improving knn computational performance on clinical trials text classification. Big Data Cogn. Comput., 5.","DOI":"10.3390\/bdcc5040060"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kanavos, A., Iakovou, S.A., Sioutas, S., and Tampakas, V. (2018). Large scale product recommendation of supermarket ware based on customer behaviour analysis. Big Data Cogn. Comput., 2.","DOI":"10.3390\/bdcc2020011"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Didi, Y., Walha, A., and Wali, A. (2022). COVID-19 Tweets Classification Based on a Hybrid Word Embedding Method. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020058"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ebrahimi, P., Basirat, M., Yousefi, A., Nekmahmud, M., Gholampour, A., and Fekete-farkas, M. (2022). Social Networks Marketing and Consumer Purchase Behavior: The Combination of SEM and Unsupervised Machine Learning Approaches. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020035"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.puhe.2022.09.008","article-title":"Public sentiment on the global outbreak of monkeypox: An unsupervised machine learning analysis of 352,182 twitter posts","volume":"213","author":"Ng","year":"2022","journal-title":"Public Health"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.eswa.2017.08.049","article-title":"W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis","volume":"91","author":"Cuadros","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.procs.2020.03.325","article-title":"Sentiment analysis of financial news using unsupervised approach","volume":"167","author":"Yadav","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kaur, G., Kaushik, A., and Sharma, S. (2019). Cooking is creating emotion: A study on hinglish sentiments of youtube cookery channels using semi-supervised approach. Big Data Cogn. Comput., 3.","DOI":"10.3390\/bdcc3030037"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Macrohon, J.J.E., Villavicencio, C.N., Inbaraj, X.A., and Jeng, J. (2022). A Semi-Supervised Approach to Sentiment Analysis of Tweets during the 2022 Philippine Presidential Election. Information, 13.","DOI":"10.3390\/info13100484"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Deng, Y., Zhang, C., Yang, N., and Chen, H. (2022). FocalMatch: Mitigating Class Imbalance of Pseudo Labels in Semi-Supervised Learning. Appl. Sci., 12.","DOI":"10.3390\/app122010623"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.neunet.2021.11.026","article-title":"Deep semi-supervised learning via dynamic anchor graph embedding in latent space","volume":"146","author":"Tu","year":"2022","journal-title":"Neural Networks"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.ins.2021.12.076","article-title":"ReCom: A deep reinforcement learning approach for semi-supervised tabular data labeling","volume":"589","author":"Zaks","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Riyadh, M., and Omair Shafiq, M. (2021, January 15\u201318). Towards Multi-class Sentiment Analysis with Limited Labeled Data. Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA.","DOI":"10.1109\/BigData52589.2021.9671692"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107345","DOI":"10.1016\/j.knosys.2021.107345","article-title":"A deep learning approach for semi-supervised community detection in Online Social Networks","volume":"229","author":"Galli","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"189287","DOI":"10.1109\/ACCESS.2020.3031665","article-title":"SEML: A semi-supervised multi-task learning framework for aspect-based sentiment analysis","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, H., Zhang, J., Suzuki, Y., Fukumoto, F., and Nishizaki, H. (2021, January 28\u201330). Semi-Supervised Learning for Aspect-Based Sentiment Analysis. Proceedings of the 2021 International Conference on Cyberworlds (CW), Caen, France.","DOI":"10.1109\/CW52790.2021.00042"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ilmania, A., Cahyawijaya, S., and Purwarianti, A. (2018, January 15\u201317). Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-Based Sentiment Analysis. Proceedings of the 2018 International Conference on Asian Language Processing, Bandung, Indonesia.","DOI":"10.1109\/IALP.2018.8629181"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Cahyadi, A., and Khodra, M.L. (2018, January 14\u201317). Aspect-Based Sentiment Analysis Using Convolutional Neural Network and Bidirectional Long Short-Term Memory. Proceedings of the 2018 5th International Conference on Advanced Informatics: Concept Theory and Applications (ICAICTA), Krabi, Thailand.","DOI":"10.1109\/ICAICTA.2018.8541300"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wahyudi, E., and Kusumaningrum, R. (2019, January 29\u201330). Aspect Based Sentiment Analysis in E-Commerce User Reviews Using Latent Dirichlet Allocation (LDA) and Sentiment Lexicon. Proceedings of the 3rd International Conference on Informatics and Computational Sciences, Semarang, Indonesia.","DOI":"10.1109\/ICICoS48119.2019.8982522"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kim, D., Kim, Y.J., and Jeong, Y.S. (2022). Graph Convolutional Networks with POS Gate for Aspect-Based Sentiment Analysis. Appl. Sci., 12.","DOI":"10.3390\/app121910134"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"An, W., Tian, F., Chen, P., and Zheng, Q. (IEEE Trans. Comput. Soc. Syst., 2022). Aspect-Based Sentiment Analysis with Heterogeneous Graph Neural Network, IEEE Trans. Comput. Soc. Syst., early access.","DOI":"10.1109\/TCSS.2022.3148866"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110025","DOI":"10.1016\/j.knosys.2022.110025","article-title":"Integrating external knowledge into aspect-based sentiment analysis using graph neural network","volume":"259","author":"Gu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, J., Dai, A., Xue, Y., Zeng, B., and Liu, X. (2022). Syntactically Enhanced Dependency-POS Weighted Graph Convolutional Network for Aspect-Based Sentiment Analysis. Mathematics, 10.","DOI":"10.3390\/math10183353"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107736","DOI":"10.1016\/j.knosys.2021.107736","article-title":"Phrase dependency relational graph attention network for Aspect-based Sentiment Analysis","volume":"236","author":"Wu","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_39","unstructured":"Wang, K., Shen, W., Yang, Y., Quan, X., and Wang, R. Relational Graph Attention Network for Aspect-based Sentiment Analysis. Proceedings of the Association for Computational Linguistics."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nayoan, R.A.N., Fathan Hidayatullah, A., and Fudholi, D.H. (2021, January 3\u20135). Convolutional Neural Networks for Indonesian Aspect-Based Sentiment Analysis Tourism Review. Proceedings of the 9th International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia.","DOI":"10.1109\/ICoICT52021.2021.9527518"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tedjojuwono, S.M., and Neonardi, C. (2021, January 28). Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language. Proceedings of the 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), Jakarta, Indonesia.","DOI":"10.1109\/ICCSAI53272.2021.9609794"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Manik, L.P., Febri Mustika, H., Akbar, Z., Kartika, Y.A., Ridwan Saleh, D., Setiawan, F.A., and Atman Satya, I. (2020, January 18\u201320). Aspect-Based Sentiment Analysis on Candidate Character Traits in Indonesian Presidential Election. Proceedings of the 2020 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), Tangerang, Indonesia.","DOI":"10.1109\/ICRAMET51080.2020.9298595"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yanuar, M.R., and Shiramatsu, S. (2020, January 19\u201321). Aspect Extraction for Tourist Spot Review in Indonesian Language using BERT. Proceedings of the 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan.","DOI":"10.1109\/ICAIIC48513.2020.9065263"},{"key":"ref_44","unstructured":"Chakraborty, A. (2022). Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, R., Chen, H., Feng, F., Ma, Z., Wang, X., and Hovy, E. (2021, January 1\u20136). Dual graph convolutional networks for aspect-based sentiment analysis. Proceedings of the ACL-IJCNLP 2021\u201459th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Proceedings of the Conference, Online.","DOI":"10.18653\/v1\/2021.acl-long.494"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"107643","DOI":"10.1016\/j.knosys.2021.107643","article-title":"Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks","volume":"235","author":"Liang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.inffus.2022.10.004","article-title":"Aspect-level sentiment analysis: A survey of graph convolutional network methods","volume":"91","author":"Phan","year":"2023","journal-title":"Inf. Fusion"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/5\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:54:33Z","timestamp":1760147673000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/7\/1\/5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,28]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["bdcc7010005"],"URL":"https:\/\/doi.org\/10.3390\/bdcc7010005","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,28]]}}}