@Article{jnm.2021.018923,
AUTHOR = {Zheng Zhang, Shu Zhou},
TITLE = {Research on Feature Extraction Method of Social Network Text},
JOURNAL = {Journal of New Media},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {73--80},
URL = {http://www.techscience.com/JNM/v3n2/42391},
ISSN = {2579-0129},
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.},
DOI = {10.32604/jnm.2021.018923}
}