Text Data Mining Algorithm Combining CNN and DBM Models
The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To s...
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Hindawi Limited
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/9242857986ec4afb9f87fa881a44e517 |
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| Sumario: | The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining. |
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