Abstract
This paper compares two topic modeling algorithms - Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), and a feature selection algorithm chi-square to extract news feature words. After feature extraction, the three classifiers (Logistics Regression, Naive Bayes and SVM) are compared in news classification. Based on the test results, combined LSI and Logistics Regression gives the highest result compared to the other algorithms, with precision of 96% and recall of 95%.
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Sun, Y., Platoš, J. (2020). Text Classification Based on Topic Modeling and Chi-square. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_56
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DOI: https://doi.org/10.1007/978-981-15-3308-2_56
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