基于自然语言处理和机器学习的疑似土壤污染企业识别

针对污染场地识别的精准性不高、科学性不足、全面性不够和数据共享难度大等问题，以南方某地级市为研究区，借助大数据平台，基于自然语言处理和机器学习，通过引入摘要中热词权重构建改进型朴素贝叶斯模型，并对兴趣点(POI)数据进行中类行业预测和污染企业识别。结果表明，与随机森林算法和XGBoost算法相比，朴素贝叶斯算法的性能最佳；企业名称+经营范围构建有语义词汇库后，朴素贝叶斯算法的准确率、召回率和综合评价指标(F₁)值得到大幅提升，分别提高了0.23、0.23和0.23；采用权重1.27和平滑参数α为1.10后，建立了改进型朴素贝叶斯模型，实现了行业类别预测，相应的准确率、召回率和F₁值分别为0.63、0.62和0.63；识别出研究区中26个疑似土壤污染行业有关1774家企业。改进型朴素贝叶斯模型能够有效地预测疑似土壤污染企业，具有较好的准确率与召回率，能够为场地污染识别与风险管控实践提供理论依据和设计参数。

Natural language processing and machine learning-based suspected soil contamination enterprise identification

Aiming at the problems of low accuracy, inadequate scientific basis, bad wholeness and the difficulty in data sharing of soil contamination identification, a typical city in South China was selected as the research area. Based on the natural language processing and machine learning, an improved naive Bayesian model was constructed by the weights of hot words from an abstract and then utilized to predict the middle-class industries and identify the relevant contamination enterprises from point of interest (POI) data with a big data platform. The results showed that the performance of the naive Bayesian aggregation was better than that of random forest and XGBoost aggregations; the precision, recall and F1 values of the naive Bayesian aggregation were improved by 0.23, 0.23 and 0.23 after the semantic vocabulary database was constructed by enterprise name and business scope; the naive Bayesian model that constructed under the weight of 1.27 and smoothing parameter α value of 1.10 could be used for the prediction of the middle-class industries with the precision, recall and F1 value of 0.63, 0.62 and 0.63, respectively, and 1774 suspected soil contamination enterprises affiliated to 26 industry categories were identified in the research area. Therefore, the improved naive Bayesian model with the good precision and recall values can be effectively used to predict the suspected contamination enterprises, and provides the theoretical bases and design parameters for site contamination identification and risk management.