Research on feature classification method of network text data based on association rules

ABSTRACT

Due to the large number of features, sparse data, low precision of feature extraction, and long time-consuming. Using the current method to classify the text data features, it is difficult to achieve better results. A method of feature classification of network text data based on association rules is proposed. A single word is used as a classification feature to extract the association feature item of text data. The feature item after dimension reduction is to construct classifier. Through the support vector machine algorithm, the network text data feature classification is realized. The highest precision ratio of Hangxia et al.’s study [Hangxia Z, Jiajun Y, Huan R. Text categorization based on deep belief network. Comput Eng Sci. 2016;38(5):871–876] is 69%, the highest precision ratio of Wenjuan et al.’s study [Wenjuan S, Shun L, Fei Y. Iterative text classification framework based on background learning. Comput Eng Applic. 2015;51(9):129–134] is 85%, and the highest precision ratio of the proposed method is 93%. The precision of this method is higher, which shows that the method can accurately reflect the feature class information of text data and reduce the error rate of text classification. Experimental results show that the proposed method can improve the accuracy of classification results and has high robustness.

KEYWORDS:

• Association rules
• network text data
• support vector machine

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Hongben Huang

Hongben Huang (1976-), Male (Han), Wuzhou, Guangxi, China, master's degree, associate professor. His research areas are data mining and image processing, works at Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System, Wuzhou University, Wuzhou, People's Republic of China.