Open Access

ARTICLE

Research on Feature Extraction Method of Social Network Text

Zheng Zhang^*, Shu Zhou

School of Computer & Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

* Corresponding Author: Zheng Zhang. Email:

Journal of New Media 2021, 3(2), 73-80. https://doi.org/10.32604/jnm.2021.018923

Received 26 March 2021; Accepted 30 March 2021; Issue published 23 April 2021

Abstract

The development of various applications based on social network text is in full swing. Studying text features and classifications is of great value to extract important information. This paper mainly introduces the common feature selection algorithms and feature representation methods, and introduces the basic principles, advantages and disadvantages of SVM and KNN, and the evaluation indexes of classification algorithms. In the aspect of mutual information feature selection function, it describes its processing flow, shortcomings and optimization improvements. In view of its weakness in not balancing the positive and negative correlation characteristics, a balance weight attribute factor and feature difference factor are introduced to make up for its deficiency. The experimental stage mainly describes the specific process: the word segmentation processing, to disuse words, using various feature selection algorithms, including optimized mutual information, and weighted with TF-IDF. Under the two classification algorithms of SVM and KNN, we compare the merits and demerits of all the feature selection algorithms according to the evaluation index. Experiments show that the optimized mutual information feature selection has good performance and is better than KNN under the SVM classification algorithm. This experiment proves its validity.

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