Open Access

ARTICLE

News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

Zhuo Zhou¹, Jiaohua Qin^1,*, Xuyu Xiang¹, Yun Tan¹, Qiang Liu¹, Neal N. Xiong²

1 College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha, 410114, China.
2 Department of Mathematics and Computer Science, Northeastern State University, OK, 74464, USA.

* Corresponding Author: Jiaohua Qin. Email:

Computers, Materials & Continua 2020, 62(1), 217-231. https://doi.org/10.32604/cmc.2020.06431

Abstract

Due to the slow processing speed of text topic clustering in stand-alone architecture under the background of big data, this paper takes news text as the research object and proposes LDA text topic clustering algorithm based on Spark big data platform. Since the TF-IDF (term frequency-inverse document frequency) algorithm under Spark is irreversible to word mapping, the mapped words indexes cannot be traced back to the original words. In this paper, an optimized method is proposed that TF-IDF under Spark to ensure the text words can be restored. Firstly, the text feature is extracted by the TF-IDF algorithm combined CountVectorizer proposed in this paper, and then the features are inputted to the LDA (Latent Dirichlet Allocation) topic model for training. Finally, the text topic clustering is obtained. Experimental results show that for large data samples, the processing speed of LDA topic model clustering has been improved based Spark. At the same time, compared with the LDA topic model based on word frequency input, the model proposed in this paper has a reduction of perplexity.

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Citations