A novel method of Chinese text content analysis and mining based on statistical models

Kaixiang Jiao

doi:10.1117/12.2672561

28 March 2023 A novel method of Chinese text content analysis and mining based on statistical models

Kaixiang Jiao

Proceedings Volume 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022); 1259725 (2023) https://doi.org/10.1117/12.2672561
Event: Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 2022, Nanjing, China

Abstract

With the accumulation of various kinds of text data, it is no longer possible to generalize or classify them by manual reading, so how to use statistical models to mine text data reasonably and effectively has become an important issue in academic research and practical work. This paper discusses three problems of Chinese text mining: word separation, keyword extraction and text classification. For the word separation problem, the Cascaded Hidden Markov Model and the WDM that treats the segmentation between words as missing data and solves it with the EM algorithm are introduced. For the keyword extraction problem, this paper proposes a Bayes factor and introduces CCS using sparse regression. For the text classification problem, the method of building a classifier based on the frequency of keywords and the method of building a classifier based on the probability of the topic first are introduced. We give the respective advantages of each method by comparing the above methods with two datasets using SVM and Random Forest and make suggestions of their use.

Citation Download Citation

Kaixiang Jiao "A novel method of Chinese text content analysis and mining based on statistical models", Proc. SPIE 12597, Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), 1259725 (28 March 2023); https://doi.org/10.1117/12.2672561

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