Abstract
Text classification based on thresholds belongs to the supervised learning method which assigns text material to predefined classes or categories based on different thresholds with divergence approach. These categories are identified by a set of documents trained by an automated algorithm. This work presents an approach of text classification using an automatic keyword extraction algorithm based on the Kullback–Leibler divergence approach. The proposed method is evaluated on 2000 documents in Vietnamese, covering ten topics, collected from various e-journals and news portal Web sites including vietnamnet.vn, vnexpress.net, and so on to generate a completely new set of keywords. Such keywords, then, are leveraged to categorize the topic of new text documents. The obtained results verifying the practicality of our approach are feasible as well as outperform the state-of-the-art method.
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Thi Tran, T. C., Huynh, H. X., Tran, P. Q., & Truong, D. Q. (2019). Text classification based on keywords with different thresholds. In ACM International Conference Proceeding Series.
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Huynh, H.X., Phan, C.A., Tran, T.C.T., Nguyen, H.T., Truong, D.Q. (2023). Threshold Text Classification with Kullback–Leibler Divergence Approach. In: Nguyen, T.D.L., Lu, J. (eds) Machine Learning and Mechanics Based Soft Computing Applications. Studies in Computational Intelligence, vol 1068. Springer, Singapore. https://doi.org/10.1007/978-981-19-6450-3_2
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