{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:31:27Z","timestamp":1785544287557,"version":"3.56.0"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T00:00:00Z","timestamp":1648512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71701142"],"award-info":[{"award-number":["71701142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2018M640346"],"award-info":[{"award-number":["2018M640346"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tianjin Philosophy and Social Science Planning Project","award":["TJKS19XSX-015"],"award-info":[{"award-number":["TJKS19XSX-015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>There exist various text-classification tasks using user-generated contents (UGC) on social media in the big data era. In view of advantages and disadvantages of feature-engineering-based machine-learning models and deep-learning models, we argue that fusing handcrafted-text representation via feature engineering and data-driven deep-text representations extracted by performing deep-learning methods is conducive to enhancing text-classification capability. Given the characteristics of different deep neural networks, their complementary effect needs to be investigated. Moreover, contributions of these representations need to be adaptively learned when it comes to addressing different tasks or predicting different samples. Therefore, in this paper, we propose a novel fused deep-neural-network architecture with a hierarchical attention mechanism for text classification with social media data. Specifically, in the context that handcraft features are available, we employ the attention mechanism to adaptively fuse totally data-driven-text representation and handcrafted representation. For the generation of the data-driven-text representation, we propose a data-driven encoder that fuses text representations derived from three deep-learning methods with the attention mechanism, to adaptively select discriminative representation and explore their complementary effect. To verify the effectiveness of our approach, we performed two text-classification tasks, i.e., identifying adverse drug reaction (ADR)-relevant tweets from social media and identifying comparative-relevant reviews from an E-commerce platform. Experimental results demonstrate that our approach outperforms other baselines.<\/jats:p>","DOI":"10.3390\/info13040171","type":"journal-article","created":{"date-parts":[[2022,3,29]],"date-time":"2022-03-29T21:44:52Z","timestamp":1648590292000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Attention-Based Multi-Representational Fusion Method for Social-Media-Based Text Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"},{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihua","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Management, Fudan University, Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin 300222, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102730","DOI":"10.1016\/j.ijhm.2020.102730","article-title":"User generated content for exploring factors affecting intention to use travel and food delivery services","volume":"92","author":"Ray","year":"2021","journal-title":"Int. J. Hosp. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"126814","DOI":"10.1016\/j.jclepro.2021.126814","article-title":"Public attention and sentiment of recycled water: Evidence from social media text mining in China","volume":"303","author":"Li","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"30193","DOI":"10.1109\/ACCESS.2021.3059960","article-title":"Explainable machine learning exploiting news and domain-specific lexicon for stock market forecasting","volume":"9","author":"Carta","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7024","DOI":"10.1109\/JIOT.2020.3037109","article-title":"ComPath: User interest mining in heterogeneous signed social networks for Internet of people","volume":"8","author":"Dhelim","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, J., Wang, X., and Huang, L. (2021, January 24\u201326). Fusing Various Document Representations for Comparative Text Identification from Product Reviews. Proceedings of the International Conference on Web Information Systems and Applications, Kaifeng, China.","DOI":"10.1007\/978-3-030-87571-8_46"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.future.2019.03.015","article-title":"Identifying malicious social media contents using multi-view context-aware active learning","volume":"100","author":"Bhattacharjee","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"107023","DOI":"10.1016\/j.asoc.2020.107023","article-title":"Analyzing the effectiveness of semi-supervised learning approaches for opinion spam classification","volume":"101","author":"Ligthart","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.jbi.2014.11.002","article-title":"Portable automatic text classification for adverse drug reaction detection via multi-corpus training","volume":"53","author":"Sarker","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1016\/j.jbi.2015.01.011","article-title":"Filtering big data from social media\u2013Building an early warning system for adverse drug reactions","volume":"54","author":"Yang","year":"2015","journal-title":"J. Biomed. Inform."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1177\/0165551517740835","article-title":"Analysis of user-generated content from online social communities to characterise and predict depression degree","volume":"44","author":"Fatima","year":"2018","journal-title":"J. Inf. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bing, C., Wu, Y., Dong, F., Xu, S., Liu, X., and Sun, S. (2022). Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection. Information, 13.","DOI":"10.3390\/info13010025"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1002\/asi.24096","article-title":"Collecting event-related tweets from twitter stream","volume":"70","author":"Zheng","year":"2019","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"ref_13","unstructured":"Li, Q., Peng, H., Li, J., Xia, C., Yang, R., Sun, L., Yu, P.S., and He, L. (2020). A Survey on Text Classification: From Shallow to Deep Learning. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439726","article-title":"Deep learning--based text classification: A comprehensive review","volume":"54","author":"Minaee","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_15","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102206","DOI":"10.1016\/j.ipm.2020.102206","article-title":"Feature engineering vs. deep learning for paper section identification: Toward applications in Chinese medical literature","volume":"57","author":"Zhou","year":"2020","journal-title":"Inf. Processing Manag."},{"key":"ref_17","unstructured":"Xie, J., Liu, X., Zeng, D.D., and Fang, X. (2021, December 01). Understanding medication Nonadherence from Social Media: A sentiment-Enriched Deep Learning Approach. Available online: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3091923."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"107792","DOI":"10.1016\/j.asoc.2021.107792","article-title":"Sentiment classification using attention mechanism and bidirectional long short-term memory network","volume":"112","author":"Wu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_21","unstructured":"Zhang, X., Zhao, J., and LeCun, Y. (2015). Character-level convolutional networks for text classification. Adv. Neural Inf. Process. Syst., 649\u2013657."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vu, N.T., Adel, H., Gupta, P., and Sch\u00fctze, H. (2016, January 12\u201317). Combining Recurrent and Convolutional Neural Networks for Relation Classification. Proceedings of the NAACL 2016, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1065"},{"key":"ref_23","unstructured":"Yin, W., Kann, K., Yu, M., and Sch\u00fctze, H. (2017). Comparative Study of CNN and RNN for Natural Language Processing. arXiv."},{"key":"ref_24","unstructured":"Matthew, E.P., Mark, N., Mohit, I., Matt, G., Christopher, C., Kenton, L., and Luke, Z. (2018). Deep contextualized word representations. arXiv."},{"key":"ref_25","unstructured":"Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I. (2022, March 03). Improving language understanding by generative pre-training. Available online: https:\/\/s3-us-west-2.amazonaws.com\/openai-assets\/research-covers\/language-unsupervised\/language_understanding_paper.pdf."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, S., Li, Y., Feng, S., Tian, H., Wu, H., and Wang, H. (2020, January 7\u201312). Ernie 2.0: A continual pre-training framework for language understanding. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i05.6428"},{"key":"ref_27","unstructured":"Sun, Y., Wang, S., Li, Y., Feng, S., Chen, X., Zhang, H., Tian, X., Zhu, D., Tian, H., and Wu, H. (2019). Ernie: Enhanced representation through knowledge integration. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gururangan, S., Marasovi\u0107, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., and Smith, N.A. (2020). Don\u2032t stop pretraining: Adapt language models to domains and tasks. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, L.-C., Lai, K.R., and Zhang, X. (2016, January 7\u201312). Dimensional sentiment analysis using a regional CNN-LSTM model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Berlin, Germany.","DOI":"10.18653\/v1\/P16-2037"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.neucom.2019.01.078","article-title":"Bidirectional LSTM with attention mechanism and convolutional layer for text classification","volume":"337","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3465055","article-title":"An attentive survey of attention models","volume":"12","author":"Chaudhari","year":"2021","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"116626","DOI":"10.1016\/j.eswa.2022.116626","article-title":"Multi-modality helps in crisis management: An attention-based deep learning approach of leveraging text for image classification","volume":"195","author":"Ahmad","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_33","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv."},{"key":"ref_34","unstructured":"Hermann, K.M., Kocisky, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., and Blunsom, P. (2015). Teaching machines to read and comprehend. Adv. Neural Inf. Process. Syst., 1693\u20131701."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., and Hovy, E. (2016, January 12\u201317). Hierarchical Attention Networks for Document Classification. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, CA, USA.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","article-title":"ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis","volume":"115","author":"Basiri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_37","unstructured":"Kiela, D., Wang, C., and Cho, K. (2008). Dynamic meta-embeddings for improved sentence representations. arXiv."},{"key":"ref_38","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. Adv. Neural Inf. Process. Syst., 6000\u20136010."},{"key":"ref_39","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv."},{"key":"ref_40","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 2014 Conference on Empirical Methods in Natural Language Processing, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/171\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:46:04Z","timestamp":1760136364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/4\/171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,29]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["info13040171"],"URL":"https:\/\/doi.org\/10.3390\/info13040171","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,29]]}}}