Analysis Of Laptop Network Scoring Based On Text Mining

With the rapid development of the Internet and the rapid rise of e-commerce,online shopping has become a trend.The arrival of the era of big data promotes the exponential growth of data.In this large amount of data that contains a lot of valuable potential information,it is very difficult to obtain only by manual reading.In this case,text mining technology emerges as the times require.Text review mining mainly includes text sentiment orientation analysis,text feature mining,subjective content recognition,etc.Among them,sentiment orientation analysis is to judge the subjective attitude of users in text data.The commonly used methods are emotional dictionary and machine learning.Foreign countries have made some achievements in the field of text mining,while domestic research in this field is still in its infancy.In recent years,the development of e-commerce has promoted the research of related technologies in the field of text mining.This paper mainly uses naive Bayesian method and topic model to mine and analyze the notebook computer review data.Firstly,the Spyder software is used to crawl the comment data of Lenovo 330 C and Dell ling yue 14 brands from the official website of Jing dong Mall as the analysis object;secondly,the comment is cleaned and preprocessed;secondly,thedocument entry matrix is formed based on the processed data space vector representation,and the dimension reduction is achieved by feature extraction;then,the classifier is constructed by Naive Bayesian method,the review data are categorized into two categories: positive and negative reviews.Finally,the model is used to extract the theme of positive and negative reviews,and to further analyze the advantages and disadvantages of user-identified products.According to the results of the analysis,mainly for manufacturers to further improve the quality of goods to meet consumer needs to put forward guiding suggestions;at the same time,it also has a certain significance for potential consumers to purchase decision-making to provide a reasonable reference.