Abstract
To solve the problem of feature sparseness of short texts, we studied the application of LDA Topic Model on feature extension and classification of short texts. Training LDA on external long texts related to short texts, and achieving the inference and extension of short texts’ topics based on LDA solves the feature sparseness of short texts and improves the accuracy of classification effectively. Latent semantic information in LDA can also effectively improve the interpretability of short texts.
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