情感分类器结合Norton模型预测汽车销量  

Sentiment Classifier Combined With Norton Model to Predict Car Sales

作　　者：顾洪建[1] 张帆 万甜甜 张衡 Gu Hongjian;Zhang Fan;Wan Tiantian;Zhang Heng(不详)

机构地区：[1]中国汽车技术研究中心有限公司,中汽研(天津)汽车信息咨询有限公司,天津市300300

出　　处：《时代汽车》2021年第3期165-167,共3页Auto Time

摘　　要：为了在"互联网+大数据+人工智能+区块链+物联网"高度信息化的社会精准预测汽车销量,本文首先利用词图、维特比等算法对汽车评价内容进行分词操作来获取关键词语;其次利用朴素贝叶斯分类器的方法对分词的结果进行计算,获得每条评论内容的情感指数;再次利用Norton模型的三代产品模型结合情感指数来组成拟合模型,同时利用最小二乘原理估计拟合模型的参数;最后利用估计的参数结合某款汽车的评论数据以及每个季度的汽车销量来验证模型,验证结果的准确性高达91.29%。基于此模型,企业可进行车型的销量预测,为合理规划生产和战略布局提供参考和依据。In order to accurately predict the sales of cars in a highly informatized society of"Internet+Big Data+Artificial Intelligence+Blockchain+Internet of Things",this article first uses word graphs,Viterbi and other algorithms to segment the car evaluation content to obtain the keywords;secondly,the article uses the naive Bayes classifier method to calculate the result of word segmentation to obtain the sentiment index of each review content;thirdly the article uses the three-generation product model of the Norton model combined with the sentiment index to form a fitting model,while the principle of the square method is used to estimate the fifteen parameters of the fitting model;finally,the estimated parameters are combined with the review data of a certain car and the car sales of each quarter to verify the model;the accuracy of the verification results is as high as 91.29%.This model can basically meet the actual forecasting needs,and can provide reference and basis for the reasonable production planning of the enterprise.

关 键 词：词图 维特比 情感指数 朴素贝叶斯 Norton模型 最小二乘法 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]