{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:16:25Z","timestamp":1769829385520,"version":"3.49.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>Aiming at the low effectiveness of short texts feature extraction, this paper proposes a short texts classification model based on the improved Wasserstein-Latent Dirichlet Allocation (W-LDA), which is a neural network topic model based on the Wasserstein Auto-Encoder (WAE) framework. The improvements of W-LDA are as follows: Firstly, the Bag of Words (BOW) input in the W-LDA is preprocessed by Term Frequency\u2013Inverse Document Frequency (TF-IDF); Subsequently, the prior distribution of potential topics in W-LDA is replaced from the Dirichlet distribution to the Gaussian mixture distribution, which is based on the Variational Bayesian inference; And then the sparsemax function layer is introduced after the hidden layer inferred by the encoder network to generate a sparse document-topic distribution with better topic relevance, the improved W-LDA is named the Sparse Wasserstein-Variational Bayesian Gaussian mixture model (SW-VBGMM); Finally, the document-topic distribution generated by SW-VBGMM is input to BiGRU (Bidirectional Gating Recurrent Unit) for the deep feature extraction and the short texts classification. Experiments on three Chinese short texts datasets and one English dataset represent that our model is better than some common topic models and neural network models in the four evaluation indexes (accuracy, precision, recall, F1 value) of text classification.<\/jats:p>","DOI":"10.3233\/jifs-211471","type":"journal-article","created":{"date-parts":[[2021,12,24]],"date-time":"2021-12-24T10:26:43Z","timestamp":1640341603000},"page":"2143-2155","source":"Crossref","is-referenced-by-count":6,"title":["The short texts classification based on neural network topic model"],"prefix":"10.1177","volume":"42","author":[{"given":"Dangguo","family":"Shao","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Chengyao","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Chusheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Qing","family":"An","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"}]},{"given":"Yan","family":"Xiang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Junjun","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]},{"given":"Jianfeng","family":"He","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China"},{"name":"Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technoloy, Kunming, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-211471_ref2","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.4028\/www.scientific.net\/AMR.971-973.1747","article-title":"Short Text Clustering Algorithms for Weibo Topic Detection[J]","volume":"971\u2013973","author":"Zhang","year":"2014","journal-title":"Advanced Materials Research"},{"issue":"14","key":"10.3233\/JIFS-211471_ref3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.4236\/jcc.2014.214006","article-title":"Research of Collaborative Filtering Recommendation Algorithm for Short Text[J]","volume":"2","author":"Chao","year":"2014","journal-title":"Journal of Computer & Communications"},{"issue":"3","key":"10.3233\/JIFS-211471_ref5","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"issue":"17","key":"10.3233\/JIFS-211471_ref6","doi-asserted-by":"crossref","first-page":"3728","DOI":"10.3390\/s19173728","article-title":"A Method of Short Text Representation Based on the Feature Probability Embedded Vector[J]","volume":"19","author":"Zhou","year":"2019","journal-title":"Sensors"},{"issue":"1","key":"10.3233\/JIFS-211471_ref7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13278-020-00699-8","article-title":"Topics extraction in incremental short texts based on LSTM[J]","volume":"10","author":"Zhang","year":"2020","journal-title":"Social Network Analysis and Mining"},{"issue":"2","key":"10.3233\/JIFS-211471_ref8","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1049\/cje.2020.01.001","article-title":"A Short Text Classification Method Based on N-Gram and CNN[J]","volume":"29","author":"Wang","year":"2020","journal-title":"Chinese Journal of Electronics"},{"issue":"12","key":"10.3233\/JIFS-211471_ref9","doi-asserted-by":"crossref","first-page":"2928","DOI":"10.1109\/TKDE.2014.2313872","article-title":"BTM: Topic Modeling over Short Texts[J]","volume":"26","author":"Cheng","year":"2014","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"issue":"04","key":"10.3233\/JIFS-211471_ref10","first-page":"123","article-title":"Short text classification based on expanding feature of LDA[J]","volume":"51","author":"LV","year":"2015","journal-title":"Computer Engineering and Applications"},{"issue":"4","key":"10.3233\/JIFS-211471_ref11","doi-asserted-by":"crossref","first-page":"100","DOI":"10.4018\/IJGHPC.2016100106","article-title":"MR-LDA: An Efficient Topic Model for Classification of Short Text in Big Social Data[J]","volume":"8","author":"Pang","year":"2016","journal-title":"International Journal of Grid and High Performance Computing"},{"issue":"DEC.1","key":"10.3233\/JIFS-211471_ref12","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.patrec.2018.10.018","article-title":"Online Biterm Topic Model based Short Text Stream Classification using Short Text Expansion and Concept Drifting Detection[J]","volume":"116","author":"Hu","year":"2018","journal-title":"Pattern Recognition Letters"},{"key":"10.3233\/JIFS-211471_ref15","doi-asserted-by":"crossref","unstructured":"Ding R. , Nallapati R. and Xiang B. , Coherence-Aware Neural Topic Modeling\/\/ Proceedings of the Conference on Empirical Methods in Natuaral Language Processing. Brusseis, Belgium, (2018), 830\u2013836.","DOI":"10.18653\/v1\/D18-1096"},{"issue":"4","key":"10.3233\/JIFS-211471_ref20","doi-asserted-by":"crossref","first-page":"381","DOI":"10.7232\/JKIIE.2015.41.4.381","article-title":"N-gram feature selection for text classification based on symmetrical conditional probability and tf-idf","volume":"41","author":"Choi","year":"2015","journal-title":"Journal of Korean Institute of Industrial Engineers"},{"issue":"518","key":"10.3233\/JIFS-211471_ref21","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/01621459.2017.1285773","article-title":"Variational inference: A review for statisticians[J]","volume":"112","author":"Blei","year":"2017","journal-title":"Journal of the American Statistical Association"},{"key":"10.3233\/JIFS-211471_ref22","doi-asserted-by":"crossref","unstructured":"Lin T. , Hu Z. and Guo X. , Sparsemax and Relaxed Wasserstein for Topic Sparsity\/\/ proceedings of the ACM International conference on Web Search and Data Mining. Melbourne, Australia, (2019), 141\u2013149.","DOI":"10.1145\/3289600.3290957"},{"key":"10.3233\/JIFS-211471_ref23","doi-asserted-by":"crossref","unstructured":"Han Y. , Liu M. and Jing W. , Aspect-level Drug Reviews Sentiment Analysis based on Double BiGRU and Knowledge Transfer[J], IEEE Access PP(99) (2020), 1\u20131.","DOI":"10.1109\/ACCESS.2020.2969473"},{"issue":"3","key":"10.3233\/JIFS-211471_ref24","first-page":"1","article-title":"Text to image synthesis using multi-generator text conditioned generative adversarial networks","volume":"80","author":"Zhang","year":"2021","journal-title":"Multimedia Tools and Applications"},{"issue":"3","key":"10.3233\/JIFS-211471_ref25","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/TMI.2015.2495246","article-title":"Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer","volume":"35","author":"Gangeh","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.3233\/JIFS-211471_ref26","first-page":"375","article-title":"Information Diffusion Kernels","volume":"15","author":"Lafferty","year":"2002","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.3233\/JIFS-211471_ref27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3233\/JIFS-191171","article-title":"Attention-based LSTM, GRU and CNN for short text classification","volume":"39","author":"Yu","year":"2020","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"key":"10.3233\/JIFS-211471_ref28","unstructured":"Martins A.F. and Astudillo R.F. , From softmax to sparsemax: a sparse model of attention and multi-label classification\/\/ Procedings of the International Conference on Machine Learning. New York, USA, (2016), 1614\u20131623."},{"issue":"11","key":"10.3233\/JIFS-211471_ref29","first-page":"1","article-title":"Improved feature size customized fast correlation-based filter for Naive Bayes text classification[J]","volume":"38","author":"Zhang","year":"2020","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"issue":"5","key":"10.3233\/JIFS-211471_ref30","doi-asserted-by":"crossref","first-page":"3641","DOI":"10.1109\/TVT.2015.2432038","article-title":"Localizing Multiple Objects Using Radio Tomographic Imaging Technology","volume":"65","author":"Wang","year":"2016","journal-title":"IEEE Transactions on Vehicular Technology"},{"issue":"2","key":"10.3233\/JIFS-211471_ref31","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/0893-6080(94)00064-S","article-title":"An Algorithm to Generate Radial Basis Function (RBF)-Like Nets for Classification Problems","volume":"8","author":"Roy","year":"1995","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-211471_ref32","doi-asserted-by":"crossref","unstructured":"Prabhudesai K. , Mainsah B. , Collins L. and Throckmorton C.S. , Augmented Latent Dirichlet Allocation (LDA) Topic Model with Gaussian Mixture Topics\/\/ ICASSP 2018-2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, (2018), 2451\u20132455.","DOI":"10.1109\/ICASSP.2018.8462003"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-211471","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:22:59Z","timestamp":1769782979000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-211471"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,2]]},"references-count":24,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-211471","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,2]]}}}