情感分类器结合Norton模型预测汽车销量

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...

Sentiment Classifier Combined With Norton Model to Predict Car Sales

In order to accurately predict the sales of cars in a highly informatized society ofInternet+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.