Python-Based Visual Classification Algorithm for Economic Text Big Data - Yihuo Jiang; Xiaomei Guo; Hongliang Ni; Wenbing Jiang (Discrete Dynamics in Nature and Society)
In order to improve the classification accuracy and reduce the classification time of the economic text big data visualization classification algorithm, one based on the Pitton algorithm is proposed. The economic text big data are preprocessed by filtering out useless symbols, word segmentation processing, and removing stop words. According to the processing results, the most relevant features of the economic text big data classification process are selected, including Gini index, information gain, mutual information, etc., and the TF-IDF weighting algorithm is used to weight the economic text data features. Based on feature weighting, using Naive Bayesian algorithm, combining classification probability distribution and text probability distribution, Naive Bayesian classifier is constructed to obtain the optimal classification result through input vector, and the visual classification of economic text big data is completed through Python software programming. The simulation results show that the classification accuracy of the algorithm for the visual classification of economic text big data can reach 100%, and the classification time is less than 5 seconds. It has high accuracy and fast efficiency.