The DOI matches its Crossref record; 39 of the 40 references it cites were found in a registry or an index.
WTL-CNN: a news text classification method of convolutional neural network based on weighted word embedding
Open accessAbstract
The word embedding model word2vec tends to ignore the importance of a single word to the entire document, which affects the accuracy of the news text classification method. To address this problem, a method that combines word2vec, a topic-based TF-IDF algorithm, and an improved convolutional neural network is proposed in this paper, which is named WTL-CNN. Firstly, word2vec is used to convert text data into word vectors. Secondly, an improved TF-IDF algorithm is proposed to weight word vectors. The improved TF-IDF algorithm introduces LDA topic generation model to enhance the topic semantic information of TF-IDF values. Thirdly, according to the location distribution law of important features of news texts, the location information is converted into weights and integrated into the pooling process convolutional neural network to further improve the accuracy of classification. At last, WTL-CNN has been evaluated and compared with seven contrast models on datasets THUCNews and SogouCS under the environment of TensorFlow. The experimental results show that the precision rate, recall rate and F1 value of WTL-CNN model reach 95.76%, 93.43%, 94.98% respectively on the THUCNews, and reach 94.61%, 93.43%, 94.01 respectively on the SogouCS.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“By leveraging a contextual window, Word2Vec is capable of unsupervised learning to determine semantic meaning and similarity between words (Zhao et al. 2022; Subba and Kumari 2022; Oubenali et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Zhao et al. 2022
Citations
Cites
Deep Pyramid Convolutional Neural Networks for Text Categorization
Johnson, R., & Zhang, T. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1052 · Cited by 877
Latent Semantic Indexing: A Probabilistic Analysis
Papadimitriou, C. H., Raghavan, P., Tamaki, H., & Vempala, S. (2000) · Journal of Computer and System Sciences · doi:10.1006/jcss.2000.1711 · Cited by 577
WELFake: Word Embedding Over Linguistic Features for Fake News Detection
Verma, P. K., Agrawal, P., Amorim, I., & Prodan, R. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2021.3068519 · Cited by 315
2017 International Conference on Engineering and Technology (ICET)
(2017) · doi:10.1109/icet42298.2017 · Cited by 273
Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods
Liu, H., Chatterjee, I., Zhou, M., Lu, X. S., & Abusorrah, A. M. (2020) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3033302 · Cited by 252
Content-Based Movie Recommendation System Using Genre Correlation
Reddy, S., Nalluri, S., Kunisetti, S., Ashok, S., & Venkatesh, B. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-1927-3_42 · Cited by 181
A Long-Text Classification Method of Chinese News Based on BERT and CNN
Chen, X., Cong, P., & Lv, S. (2022) · IEEE Access · doi:10.1109/access.2022.3162614 · Cited by 170
Attention-based BiLSTM fused CNN with gating mechanism model for Chinese long text classification
Deng, J., Cheng, L., & Wang, Z. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2020.101182 · Cited by 166
Web page classification based on a support vector machine using a weighted vote schema
Chen, R.-C., & Hsieh, C. H. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.09.079 · Cited by 135
A Chi-Square Statistics Based Feature Selection Method in Text Classification
Zhai, Y., Song, W., Liu, X., Liu, L., & Zhao, X. S. (2018) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2018.8663882 · Cited by 135
Zheng, J., & Zheng, L. (2019) · IEEE Access · doi:10.1109/access.2019.2932619 · Cited by 106
Network text sentiment analysis method combining LDA text representation and GRU-CNN
Luo, L.-X. (2018) · Personal and Ubiquitous Computing · doi:10.1007/s00779-018-1183-9 · Cited by 102
Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification
Xie, J., Chen, B., Gu, X., Liang, F., & Xu, X. (2019) · IEEE Access · doi:10.1109/access.2019.2957510 · Cited by 99
Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification
Tan, Z., Chen, J., Kang, Q., Zhou, M., Abusorrah, A. M., & Sedraoui, K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3036192 · Cited by 93
Convolutional multi-head self-attention on memory for aspect sentiment classification
Zhang, Y., Xu, B. H., & Zhao, T. (2020) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2020.1003243 · Cited by 86
An kNN Model-Based Approach and Its Application in Text Categorization
Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24630-5_69 · Cited by 81
Research on Text Classification Based on Convolutional Neural Network
Song, P., Geng, C., & Li, Z. (2019) · International Conference on Computer Network, Electronic and Automation (ICCNEA) · doi:10.1109/iccnea.2019.00052 · Cited by 55
An emotional classification method of Chinese short comment text based on ELECTRA
Zhang, S., Yu, H., & Zhu, G. (2021) · Connection Science · doi:10.1080/09540091.2021.1985968 · Cited by 51
A text classification method based on a convolutional and bidirectional long short-term memory model
Huan, H., Guo, Z., Cai, T., & He, Z. (2022) · Connection Science · doi:10.1080/09540091.2022.2098926 · Cited by 48
Prediction of customer engagement behaviour response to marketing posts based on machine learning
Dai, Y., & Wang, T. (2021) · Connection Science · doi:10.1080/09540091.2021.1912710 · Cited by 47
Sentiment Analysis With Comparison Enhanced Deep Neural Network
Lin, Y., Li, J., Yang, L., Xu, K., & Lin, H. (2020) · IEEE Access · doi:10.1109/access.2020.2989424 · Cited by 43
Chinese Text Classification Based on Hybrid Model of CNN and LSTM
Li, X., & Ning, H. (2020) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1145/3414274.3414493 · Cited by 28
A news classification applied with new text representation based on the improved LDA
Shao, D., Li, C., Huang, C., Xiang, Y., & Yu, Z. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12713-6 · Cited by 28
Extending emotional lexicon for improving the classification accuracy of Chinese film reviews
Wang, Q., Zhu, G., Zhang, S., Li, K., Chen, X., & Xu, H. (2020) · Connection Science · doi:10.1080/09540091.2020.1782839 · Cited by 27
An Improved Information Gain Feature Selection Algorithm for SVM Text Classifier
Xu, J., & Jiang, H. (2015) · International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2015.53 · Cited by 26
Yu, L., Duan, Y., & Li, K. (2020) · Connection Science · doi:10.1080/09540091.2020.1841110 · Cited by 25
Wei, Z., Liu, W., Zhu, G., Zhang, S., & Hsieh, M.-Y. (2021) · Connection Science · doi:10.1080/09540091.2021.2006146 · Cited by 22
Microblog Emotional Analysis Based on TF-IWF Weighted Word2vec Model
Tian, H., & Wu, L. (2018) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess.2018.8663837 · Cited by 21
Improved Expected Cross Entropy Method for Text Feature Selection
Wu, G., Wang, L., Zhao, N., & Lin, H. (2015) · International Conference on Computer Science and Mechanical Automation (CSMA) · doi:10.1109/csma.2015.17 · Cited by 17
Hierarchical Comprehensive Context Modeling for Chinese Text Classification
Jin-gang, L., Xia, C., Yan, H., Xie, Z., & Sun, J. (2019) · IEEE Access · doi:10.1109/access.2019.2949175 · Cited by 17
Analysing rumours spreading considering self-purification mechanism
Zhang, Y., Liu, F., Koura, Y. H., & Wang, H. (2020) · Connection Science · doi:10.1080/09540091.2020.1783640 · Cited by 16
Research on News Text Classification Based on Deep Learning Convolutional Neural Network
Zhu, Y. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/1508150 · Cited by 16
Knowledge Discovery in Databases
Beekmann, F. (2003) · Deutscher Universitätsverlag eBooks · doi:10.1007/978-3-322-81227-8_2 · Cited by 13
Emotional Text Analysis Based on Ensemble Learning of Three Different Classification Algorithms
Bian, W., Wang, C., Ye, Z., & Yan, L. (2019) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs.2019.8924413 · Cited by 12
News Title Classification Based on Sentence-LDA Model and Word Embedding
Zhang, F., Gao, W., & Fang, Y. (2019) · International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) · doi:10.1109/mlbdbi48998.2019.00053 · Cited by 10
Liu, X., Zhang, Z., Li, B., & Zhang, F. (2019) · Chinese Automation Congress · doi:10.1109/cac48633.2019.8996936 · Cited by 7
Chang, Q. (2020) · International Journal of Computational Science and Engineering · doi:10.1504/ijcse.2020.110550 · Cited by 6
Deep convRNN for sentiment parsing of Chinese microblogging texts
Cheng, J., Zhang, S., Li, P., Zhang, X., & Wang, H. (2017) · International Conference on Computational Intelligence and Applications (ICCIA) · doi:10.1109/ciapp.2017.8167220 · Cited by 3
Self-citation
Research works printed but not linked: 8
- Amit K. S., 2020, Procedia Computer Science, 1139
- David M. B., 2003, Journal of Machine Learning Research, 3, 993
- Hofmann T., 1999, In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, 50
- Wang Q., 2019, 2019 Chinese Automation Congress (CAC), Hangzhou,1974-1978
- Wu J., 2021, 2021 IEEE International Conference on Information Communication and Software Engineering, 209
- Yu Y. J., 2021, ICT Express, 2405
- Zhang X. W., 2021, Research on News Text Classification Based on Improved BERT-CNN Model, 45, 146
- Zhou X. S., 2018, The Text Feature Representation Method Based on Word2vector, 30, 272
Cited by
Large language models (LLMs): survey, technical frameworks, and future challenges
Kumar, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10888-y · Cited by 395
Contextual Embeddings-Based Web Page Categorization Using the Fine-Tune BERT Model
Nandanwar, A. K., & Choudhary, J. (2023) · Symmetry · doi:10.3390/sym15020395 · Cited by 30
Text classification on heterogeneous information network via enhanced GCN and knowledge
Li, H., Yan, Y., Wang, S., Liu, J., & Cui, Y. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08494-0 · Cited by 24
Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms
Wang, H. (2024) · Systems and Soft Computing · doi:10.1016/j.sasc.2024.200087 · Cited by 23
Cao, K., Chen, S., Zhang, X., Chen, Y., Li, Z., & Wang, D. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.05.100 · Cited by 16
Self-citation
Metadata
- Authors
- Zhao, W., Zhu, L., Wang, M., Zhang, X., & Zhang, J.
- Affiliations
- Shandong University of Science and Technology (CN)
- Qingdao Customs Technology Center, Qingdao, People’s Republic of China
- Shandong Expressway Information Group Co., Ltd., Jinan, People’s Republic of China
- Shandong Qilu Electronic Tendering and Procurement Service Co., Ltd., Jinan, People’s Republic of China
- Corresponding author
- Wang, Ming
- Venue
- Connection Science
- Publisher
- Informa UK Limited
- Published
- 2022-08-29
- Volume
- 34
- Issue
- 1
- Pages
- 2291-2312
- ISSN
- 0954-0091 (ISSN-L)
- 1360-0494
- OpenAlex
- W4293462507
- Cited by
- 35
- References
- 40
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 13 · 2023: 12 · 2022: 2
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Connection Science (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- news classification, Word2Vec, TF-IDF weighting, LDA topic modeling, convolutional neural networks, pooling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
