Simulation of Fast Text Classification Based on Keyword Weighting
For the network information in the form of electronic text,the mixed features have high similarity,so it is difficult to achieve the average distribution of features.The non-uniformity of feature items among categories leads to the deviation in calculating text weight,affecting the extraction of category feature words and text classification.Therefore,this article presented a fast classification method for the text with hybrid features based on keyword weigh-ting.Firstly,the text was weighted by the information retrieval method based on term frequency-inverse document fre-quency index.Secondly,the frequency of text keywords in the central set was calculated under different weights.Then,key features were extracted according to the frequency threshold.Meanwhile,the final cluster center in the text set was determined.Thirdly,the text data with the highest correlation with the cluster center was calculated,and the correlation feature was extracted.After that,a neural network classification model was built.Moreover,a group of text sets containing detailed features was preset and input into the neural network as initial values.Finally,all levels were compared one by one according to the target features.Thus,effective classification was achieved.Experiment results prove that the recall rate of the method is higher,and the recall rate of mixed feature extraction of text is more than 40%,indicating that the method has better application performance,and can complete accurate classification for dif-ferent kinds of text sets.
Keywords weightingMixed feature textFrequency thresholdNeural network classification model
NETL
NSTL
万方数据
