Classification method of information flow material creative text

Abstract

The invention discloses a classification method of information flow material creative text, includes extracting frequent feature word sets and their associated training text based on association analysis and using the association analysis results, The present invention greatly reduces the time complexity and improves the accuracy of classification, improves the determination of the number of the nearest neighbors, and greatly reduces the time complexity of classification.

Description

Technical field [0001] The invention relates to the field of text classification, in particular to a method for classifying creative texts of information flow materials. Background technique [0002] With the rapid development of network information technology, Internet information resources are showing an exponential growth trend, and text is the most basic information carrier, and its classification technology has become a hot spot in modern information processing. At present, the commonly used text classification algorithms are: Naive Bayes, Support Vector Machine, Neural Network, Decision Number, K-Nearest Neighbor and other methods. Among them, the text classification method based on classic KNN is simple and effective, and it is one of the best classification methods, but it also has some obvious shortcomings: first, when determining the type of text to be classified, it needs to be calculated with all samples in the training sample set. After that, select the top k sample...

AI Technical Summary

Problems solved by technology

Among them, the text classification method based on classical KNN is simple and effective, and it is one of the methods with the best classification effect, but it also has some obvious disadvantages: First, when determining the category of the text to be classified, it is necessary to calculate its relationship with all samples in the training sample set. The similarity, and then select the top k samples with the highest similarity. In general, the training samples for text classification are often large in scale. Since the similarity with the text to be classified is calculated on tens of thousands of training samples, As the number of training samples increases, the classification performance will drop quickly; second, it is a lazy text classification learning method, which requires a large amount of calculation and consumes more time when classifying test samples. As the scale increases, the classification time increases sharply, causing the classification time to be nonlinear; thirdly, the KNN algorithm must specify a value of k, and how to determine the number of neighbors of the text to be classified is still lacking a better and widely applicable method. The selection of k Plays a very important role in category determination, if k is too large or too small, it will reduce the accuracy of text classification