Welcome to Scholar Publishing Group

Machine Learning Theory and Practice, 2023, 4(1); doi: 10.38007/ML.2023.040106.

Automatic Text Classification Model Based on Machine Learning


Jianhua Li

Corresponding Author:
Jianhua Li

Philippine Christian University, Philippine


The artificial neural network is composed of RBF algorithm, multi-layer parallel model and other parts. Its working principle is to classify the input image through layer by layer training, and then develop and determine the attributes of each neuron with similar features. This paper studies them based on Bayesian classifier, a common method in machine learning. First of all, before text recognition, we need to select the sample library and data type, and design the parameters according to the requirements to form an efficient automatic text classification. Then, according to the functional modules required by the specific model, select the appropriate language and compile and generate program code to realize the whole process, and carry out simulation tests on the functions of the model. The test results show that this test has prepared different quantities of simple words, short sentences, long articles, professional terms and colloquial expressions for classification tests. The classification accuracy of this model is as high as 90%, the error rate is low, and the classification time is fast, This shows that the model meets the needs of users.


Machine Learning, Automatic Text, Text Classification, Automatic Model

Cite This Paper

Jianhua Li. Automatic Text Classification Model Based on Machine Learning. Machine Learning Theory and Practice (2023), Vol. 4, Issue 1: 44-51. https://doi.org/10.38007/ML.2023.040106.


[1] Kadhim A I. Survey on supervised machine learning techniques for automatic text classification. Artificial Intelligence Review, 2019,52(1):273-292.

[2] Janani R, Vijayarani S. Automatic text classification using machine learning and optimization algorithms. Soft computing: A fusion of foundations, methodologies and applications, 2021,25(2):1129-1145.

[3] Asogwa D C, Anigbogu S O, Onyenwe I E, et al. Text Classification Using Hybrid Machine Learning Algorithms on Big Data.2021,6(5):127-131.

[4] Wiedemann G. Proportional Classification Revisited: Automatic Content Analysis of Political Manifestos Using Active Learning. Social science computer review, 2019, 37(2):135-159.

[5] Mumivand H, Piri R S, Kheiraei F. A New Model for Automatic Text Classification. Electrical Science and Engineering, 2021, 3(1):10-15.

[6] Stasak B, Epps J, Goecke R. Automatic Depression Classification Based on Affective Read Sentences: Opportunities for Text-Dependent Analysis. Speech Communication, 2019, 115(10):1-14.

[7] Kragelj M, Borstnar M K. Automatic classification of older electronic texts into the Universal Decimal Classification-UDC. Journal of Documentation, 2020,77(3):755-776.

[8] Janani R, Vijayarani S. Automatic text classification using machine learning and optimization algorithms. Soft computing: A fusion of foundations, methodologies and applications, 2021,25(2):1129-1145.

[9] Satj N U, Ordin B. Application of the Polyhedral Conic Functions Method in the Text Classification and Comparative Analysis. Scientific Programming, 2018, 2018(2),1-11.

[10] Mou S, Du P, Cheng Z. A Brain-inspired Information Processing Algorithm and Its Application in Text Classification. Expert Systems with Applications, 2021, 177(9):1-7.

[11] Wei Z, Gui Z, Zhang M, et al. Text GCN-SW-KNN:a Novel Collaborative Training Multi-Label Classification Method for Wms Application Themes by Considering Geographic Semantics. Earth Big Data (English), 2021, 5(1):66-89.

[12] Wang X, Tong Y. Application of an emotional classification model in e-commerce text based on an improved transformer model. PLoS ONE, 2021, 16(3):1-16.

[13] Sui Z. Application of machine learning method in text classification. Basic & clinical pharmacology & toxicology.2019,124(S1):119-120.

[14] Ngamsuriyaroj S, Taninpong P. Tree-based text stream clustering with application to spam mail classification. International Journal of Data Mining, Modelling and Management, 2018, 10(4):353-370. 

[15] Camacho D M, Collins K M, Powers R K, et al. Next-Generation Machine Learning for Biological Networks. Cell, 2018,173(7):1581-1592.

[16] Benjamin S L, AG Alán. Inverse molecular design using machine learning: Generative models for matter engineering. Science, 2018, 361(27):360-365.

[17] Butler K T, Davies D W, Hugh C, et al. Machine learning for molecular and materials science. Nature, 2018, 559(25):547-555.

[18] JF Hernández, Z Díaz, Segovia M J, et al. Machine Learning and Statistical Techniques. An Application to the Prediction of Insolvency in Spanish Non-life Insurance Companies. The International Journal of Digital Accounting Research, 2020, 9(5):1-45.