Cluster Analysis for Internet Public Sentiment in Universities by Combining Methods

Authors

  • Na Zheng Hebei Agricultural University
  • Jie Yu Wu Hebei Agricultural University

DOI:

https://doi.org/10.3991/ijes.v6i3.9670

Keywords:

subject modelsï¼› vector space modelï¼› comment extractionï¼› public opinionï¼› text clustering

Abstract


A clustering method based on the Latent Dirichlet Allocation and the VSM model to compute the text similarity is presented. The Latent Dirichlet Allocation subject models and the VSM vector space model weights strategy are used respectively to calculate the text similarity. The linear combination of the two results is used to get the text similarity. Then the k-means clustering algorithm is chosen for cluster analysis. It can not only solve the deep semantic information leakage problems of traditional text clustering, but also solve the problem of the LDA that could not distinguish the texts because of too much dimension reduction. So the deep semantic information is mined from the text, and the clustering efficiency is improved. Through the comparisons with the traditional methods, the result shows that this algorithm can improve the performance of text clustering.

Author Biographies

Na Zheng, Hebei Agricultural University

Academic Affairs Office

Jie Yu Wu, Hebei Agricultural University

College of Information Science & Technology

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Published

2018-11-08

How to Cite

Zheng, N., & Wu, J. Y. (2018). Cluster Analysis for Internet Public Sentiment in Universities by Combining Methods. International Journal of Recent Contributions from Engineering, Science & IT (iJES), 6(3), pp. 60–69. https://doi.org/10.3991/ijes.v6i3.9670

Issue

Vol. 6 No. 3 (2018)

Section

Papers

License

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