Trouble Of Vespa Mandarinia: Confirming the Buzz about Hornets

Business School, Shandong Normal University, Jinan, Shandong, 250358, China

^* Corresponding author: 765808770@qq.com

Abstract

In order to help Washington State interpret the data about Vespa mandarinia provided by the public report, and enable government agencies to adopt corresponding strategies to prioritize correct reports when resources are limited, for further investigation, this article establishes two targeted models: The first unsupervised probability prediction model. First, extract the text information of misjudgment classification in the data set, and carry out preprocessing. The data set is divided into training set and test set according to the ratio of 8:2, and the Latent Dirichlet Allocation model is trained using the misjudgment classification information in the training set. After the model training is completed, this paper makes a probability prediction on the data on the test set, and evaluates the robustness of the model through the accuracy rate on the test set. The second text similarity matching model is based on feature dimensionality reduction and extracting feature keywords as vectors. The TF-IDF algorithm is used to calculate the weight of each feature keyword in the vector to form a standard bag-of-words vector for the correct witnessing of the Vespa mandarinia report. Judge by the similarity of text similarity matching model.