{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:17:36Z","timestamp":1775031456146,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Short text classification is an important problem of natural language processing (NLP), and graph neural networks (GNNs) have been successfully used to solve different NLP problems. However, few studies employ GNN for short text classification, and most of the existing graph-based models ignore sequential information (e.g., word orders) in each document. In this work, we propose an improved sequence-based feature propagation scheme, which fully uses word representation and document-level word interaction and overcomes the limitations of textual features in short texts. On this basis, we utilize this propagation scheme to construct a lightweight model, sequential GNN (SGNN), and its extended model, ESGNN. Specifically, we build individual graphs for each document in the short text corpus based on word co-occurrence and use a bidirectional long short-term memory network (Bi-LSTM) to extract the sequential features of each document; therefore, word nodes in the document graph retain contextual information. Furthermore, two different simplified graph convolutional networks (GCNs) are used to learn word representations based on their local structures. Finally, word nodes combined with sequential information and local information are incorporated as the document representation. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of our method.<\/jats:p>","DOI":"10.3390\/a14120352","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T02:40:02Z","timestamp":1638412802000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Sequential Graph Neural Network for Short Text Classification"],"prefix":"10.3390","volume":"14","author":[{"given":"Ke","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Software, Jilin University, Changchun 130012, China"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Rui","family":"Song","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Jilin University, Changchun 130012, China"}]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"School of Artificial Intelligence, Jilin University, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.4304\/jmm.9.5.635-643","article-title":"Short Text Classification: A Survey","volume":"9","author":"Song","year":"2014","journal-title":"J. Multimed."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102121","DOI":"10.1016\/j.ipm.2019.102121","article-title":"Arabic text classification using deep learning models","volume":"57","author":"Elnagar","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tadesse, M.M., Lin, H., Xu, B., and Yang, L. (2020). Detection of suicide ideation in social media forums using deep learning. Algorithms, 13.","DOI":"10.3390\/a13010007"},{"key":"ref_4","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Meng, Y., Shen, J., Zhang, C., and Han, J. (2018, January 22\u201326). Weakly-Supervised Neural Text Classification. Proceedings of the Conference on Information and Knowledge Management, Turin, Italy.","DOI":"10.1145\/3269206.3271737"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, Q., Hu, Q., Huang, J.X., He, L., and An, W. (2017, January 7\u201321). Enhancing Recurrent Neural Networks with Positional Attention for Question Answering. Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","DOI":"10.1145\/3077136.3080699"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.procs.2018.10.474","article-title":"An Annotated Huge Dataset for Standard and Colloquial Arabic Reviews for Subjective Sentiment Analysis","volume":"142","author":"Elnagar","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Pintelas, P., and Livieris, I.E. (2020). Special issue on ensemble learning and applications. Algorithms, 13.","DOI":"10.3390\/a13060140"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Forman, G. (2008, January 26\u201330). BNS feature scaling: An improved representation over tf-idf for svm text classification. Proceedings of the Conference on Information and Knowledge Management, Napa Valley, CA, USA.","DOI":"10.1145\/1458082.1458119"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Wu, J., Zhang, H., Lin, H., Wang, F., Xu, K., and Xiong, H. (2016, January 13\u201317). Topic Modeling of Short Texts: A Pseudo-Document View. Proceedings of the Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939880"},{"key":"ref_11","unstructured":"Wang, S., and Manning, C. (2012, January 8\u201314). Baselines and Bigrams: Simple, Good Sentiment and Topic Classification. Proceedings of the Meeting of the Association for Computational Linguistics, Jeju, Korea."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s13042-010-0001-0","article-title":"Understanding bag-of-words model: A statistical framework","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mouratidis, D., and Kermanidis, K.L. (2019). Ensemble and deep learning for language-independent automatic selection of parallel data. Algorithms, 12.","DOI":"10.3390\/a12010026"},{"key":"ref_14","unstructured":"Le, Q., and Mikolov, T. (2014, January 21\u201326). Distributed Representations of Sentences and Documents. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"102481","DOI":"10.1016\/j.ipm.2020.102481","article-title":"On the cost-effectiveness of neural and non-neural approaches and representations for text classification: A comprehensive comparative study","volume":"58","author":"Cunha","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_16","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K.N. (2018, January 1\u20138). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the North American Chapter of the Association for Computational Linguistics, New Orleans, LA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102215","DOI":"10.1016\/j.ipm.2020.102215","article-title":"Pre-train, Interact, Fine-tune: A novel interaction representation for text classification","volume":"57","author":"Zheng","year":"2020","journal-title":"Inf. Process. Manag."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Muaad, A.Y., Jayappa, H., Al-antari, M.A., and Lee, S. (2021). ArCAR: A Novel Deep Learning Computer-Aided Recognition for Character-Level Arabic Text Representation and Recognition. Algorithms, 14.","DOI":"10.3390\/a14070216"},{"key":"ref_19","unstructured":"McCann, B., Bradbury, J., Xiong, C., and Socher, R. (2017, January 4\u20139). Learned in translation: Contextualized word vectors. Proceedings of the Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L. (2018, January 1\u20138). Deep contextualized word representations. Proceedings of the North American Chapter of the Association for Computational Linguistics, New Orleans, LA, USA.","DOI":"10.18653\/v1\/N18-1202"},{"key":"ref_21","unstructured":"Mikolov, T., Chen, K., Corrado, G.S., and Dean, J. (2013, January 2\u20134). Efficient Estimation of Word Representations in Vector Space. Proceedings of the International Conference on Learning Representations, Scottsdale, AZ, USA."},{"key":"ref_22","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed Representations of Words and Phrases and their Compositionality. Proceedings of the Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C. (2014, January 25\u201329). Glove: Global Vectors for Word Representation. Proceedings of the Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"78060","DOI":"10.1109\/ACCESS.2021.3083519","article-title":"Impact of EEG Parameters Detecting Dementia Diseases: A Systematic Review","volume":"9","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ortiz-Echeverri, C.J., Salazar-Colores, S., Rodr\u00edguez-Res\u00e9ndiz, J., and G\u00f3mez-Loenzo, R.A. (2019). A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network. Sensors, 19.","DOI":"10.3390\/s19204541"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Villegas-Mier, C.G., Rodriguez-Resendiz, J., \u00c1lvarez-Alvarado, J.M., Rodriguez-Resendiz, H., Herrera-Navarro, A.M., and Rodr\u00edguez-Abreo, O. (2021). Artificial Neural Networks in MPPT Algorithms for Optimization of Photovoltaic Power Systems: A Review. Micromachines, 12.","DOI":"10.3390\/mi12101260"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neucom.2013.02.018","article-title":"A review of parameter estimators and controllers for induction motors based on artificial neural networks","volume":"118","author":"Mucino","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_29","unstructured":"Liu, P., Qiu, X., and Huang, X. (2016, January 9\u201315). Recurrent neural network for text classification with multi-task learning. Proceedings of the International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_30","unstructured":"Battaglia, P.W., Hamrick, J.B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., and Faulkner, R. (2018). Relational inductive biases, deep learning, and graph networks. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cliche, M. (August, January 30). BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs. Proceedings of the Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada.","DOI":"10.18653\/v1\/S17-2094"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sun, C., Qiu, X., Xu, Y., and Huang, X. (2019, January 18\u201320). How to Fine-Tune BERT for Text Classification?. Proceedings of the China National Conference on Chinese Computational Linguistics, Kunming, China.","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Garg, S., and Ramakrishnan, G. (2020, January 16\u201320). BAE: BERT-based Adversarial Examples for Text Classification. Proceedings of the The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.498"},{"key":"ref_34","unstructured":"Zhou, J., Cui, G., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., and Sun, M. (2018). Graph neural networks: A review of methods and applications. arXiv."},{"key":"ref_35","unstructured":"Yao, L., Mao, C., and Luo, Y. (February, January 27). Graph Convolutional Networks for Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, L., Ma, D., Li, S., Zhang, X., and Wang, H. (2019, January 3\u20137). Text Level Graph Neural Network for Text Classification. Proceedings of the Empirical Methods in Natural Language Processing, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1345"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, Q., and Song, L. (2018, January 15\u201320). Sentence-State LSTM for Text Representation. Proceedings of the Meeting of the Association for Computational Linguistics, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1030"},{"key":"ref_38","unstructured":"Mihalcea, R., and Tarau, P. (2004, January 25\u201326). TextRank: Bringing Order into Text. Proceedings of the Empirical Methods in Natural Language Processing, Barcelona, Spain."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ding, K., Wang, J., Li, J., Li, D., and Liu, H. (2020, January 16\u201320). Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.399"},{"key":"ref_40","unstructured":"Liu, X., You, X., Zhang, X., Wu, J., and Lv, P. (2020, January 1\u201312). Tensor Graph Convolutional Networks for Text Classification. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yu, X., Cui, Z., Wu, S., Wen, Z., and Wang, L. (2020, January 6\u20138). Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. Proceedings of the Meeting of the Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2020.acl-main.31"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., and Yang, Q. (2018, January 23\u201327). Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN. Proceedings of the The Web Conference, Lyon, France.","DOI":"10.1145\/3178876.3186005"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.1016\/j.ipm.2019.02.018","article-title":"Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion","volume":"56","author":"Abdi","year":"2019","journal-title":"Inf. Process. Manag."},{"key":"ref_44","unstructured":"Liu, Y., Meng, F., Chen, Y., Xu, J., and Zhou, J. (2020). Depth-Adaptive Graph Recurrent Network for Text Classification. arXiv."},{"key":"ref_45","unstructured":"Kipf, T.N., and Welling, M. (2016, January 2\u20134). Semi-Supervised Classification with Graph Convolutional Networks. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_46","unstructured":"Wu, F., Souza, A.H., Zhang, T., Fifty, C., Yu, T., and Weinberger, K.Q. (2019, January 9\u201315). Simplifying Graph Convolutional Networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_47","unstructured":"Chen, M., Wei, Z., Huang, Z., Ding, B., and Li, Y. (2020, January 13\u201318). Simple and Deep Graph Convolutional Networks. Proceedings of the International Conference on Machine Learning, Online."},{"key":"ref_48","unstructured":"Zhang, X., Zhao, J., and LeCun, Y. (2015, January 7\u201312). Character-level convolutional networks for text classification. Proceedings of the Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pang, B., and Lee, L. (2005, January 25\u201330). Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales. Proceedings of the Meeting of the Association for Computational Linguistics, Ann Arbor, MI, USA.","DOI":"10.3115\/1219840.1219855"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., and Mei, Q. (2015, January 10\u201313). PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. Proceedings of the Knowledge Discovery and Data Mining, Sydney, Australia.","DOI":"10.1145\/2783258.2783307"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neunet.2016.12.008","article-title":"Self-Taught convolutional neural networks for short text clustering","volume":"88","author":"Xu","year":"2017","journal-title":"Neural Netw."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., and Mikolov, T. (2017, January 3\u20137). Bag of Tricks for Efficient Text Classification. Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain.","DOI":"10.18653\/v1\/E17-2068"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shen, D., Wang, G., Wang, W., Min, M.R., Su, Q., Zhang, Y., Li, C., Henao, R., and Carin, L. (2018, January 15\u201320). Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms. Proceedings of the Meeting of the Association for Computational Linguistics, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1041"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ragesh, R., Sellamanickam, S., Iyer, A., Bairi, R., and Lingam, V. (2021, January 8\u201312). Hetegcn: Heterogeneous graph convolutional networks for text classification. Proceedings of the Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Jerusalem, Israel.","DOI":"10.1145\/3437963.3441746"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gao, H., Chen, Y., and Ji, S. (2019, January 13\u201317). Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. Proceedings of the The Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313395"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Landro, N., Gallo, I., and La Grassa, R. (2021). Combining Optimization Methods Using an Adaptive Meta Optimizer. Algorithms, 14.","DOI":"10.3390\/a14060186"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). node2vec: Scalable Feature Learning for Networks. Proceedings of the Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_58","unstructured":"Abu-El-Haija, S., Perozzi, B., Al-Rfou, R., and Alemi, A.A. (2018, January 2\u20138). Watch Your Step: Learning Node Embeddings via Graph Attention. Proceedings of the Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Linmei, H., Yang, T., Shi, C., Ji, H., and Li, X. (2019, January 3\u20137). Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification. Proceedings of the Empirical Methods in Natural Language Processing, Hong Kong, China.","DOI":"10.18653\/v1\/D19-1488"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/12\/352\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:38:34Z","timestamp":1760168314000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/14\/12\/352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,1]]},"references-count":59,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["a14120352"],"URL":"https:\/\/doi.org\/10.3390\/a14120352","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,1]]}}}