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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.
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“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
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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
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Text classification on heterogeneous information network via enhanced GCN and knowledge
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Automatic question-answering modeling in English by integrating TF-IDF and segmentation algorithms
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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
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- The paper
- Zhao, W., Zhu, L., Wang, M., Zhang, X., & Zhang, J. (2022). WTL-CNN: a news text classification method of convolutional neural network based on weighted word embedding. Connection Science. https://doi.org/10.1080/09540091.2022.2117274
