Text Classification Based on Machine Learning and Natural Language Processing Algorithms
Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classi...
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| Published in | Wireless communications and mobile computing Vol. 2022; no. 1 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Hindawi
2022
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1530-8669 1530-8677 1530-8677 |
| DOI | 10.1155/2022/3915491 |
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| Abstract | Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%. |
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| AbstractList | Nowadays, with the development of media technology, people receive more and more information, but the current classification methods have the disadvantages of low classification efficiency and inability to identify multiple languages. In view of this, this paper is aimed at improving the text classification method by using machine learning and natural language processing technology. For text classification technology, this paper combines the technical requirements and application scenarios of text classification with ML to optimize the classification. For the application of natural language processing (NLP) technology in text classification, this paper puts forward the Trusted Platform Module (TPM) text classification algorithm. In the experiment of distinguishing spam from legitimate mail by text recognition, all performance indexes of the TPM algorithm are superior to other algorithms, and the accuracy of the TPM algorithm on different datasets is above 95%. |
| Author | Li, Zeming Li, Hui |
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| Cites_doi | 10.1016/j.eswa.2017.03.020 10.1155/2021/2955215 10.1166/asl.2017.8768 10.1109/ACCESS.2021.3063354 10.1016/j.ijmedinf.2017.12.024 10.1088/1742-6596/1764/1/012069 10.1016/j.is.2018.05.006 10.1109/TCYB.2015.2403573 10.1016/j.aej.2021.02.009 10.1200/JCO.2017.35.8_suppl.232 10.1155/2021/6645345 10.1016/j.jvs.2018.06.075 10.1007/s11633-015-0912-z 10.30534/ijatcse/2021/061012021 10.3233/JIFS-191171 10.14569/IJACSA.2021.0120589 10.4236/oalib.1107175 10.1016/S0016-5085(19)36923-9 10.1177/1094428117719322 10.1016/j.eswa.2018.03.058 10.1186/s12911-017-0556-8 10.32604/csse.2021.014234 10.1016/j.spinee.2020.05.470 10.18865/ed.29.S2.441 10.1002/cpe.4779 10.1164/rccm.201610-2006OC |
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| Copyright | Copyright © 2022 Hui Li and Zeming Li. Copyright © 2022 Hui Li and Zeming Li. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| SubjectTerms | Active learning Algorithms Artificial intelligence Classification Data mining Deep learning Internet Language Machine learning Natural language processing Performance evaluation Performance indices Social research Sports injuries Text categorization |
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| Title | Text Classification Based on Machine Learning and Natural Language Processing Algorithms |
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