Data analysis of online shopping platform during the COVID-19 epidemic of Coronavirus disease

Data analysis of online shopping platform, and With the development of online platform, more and more consumers will choose this convenient way of online shopping. This paper uses Spyder’s time-based model to mine the rating data of Amazon online shopping platform, establishes a neural network model, analyzes the connection relationship of each rating index, carries out descriptive statistical analysis on each index, obtains the correlation results between the impact indicators, and carries out fuzzy evaluation, analyzes the impact of each evaluation index on the product, and fi nally combines the product’s The relationship between sales situation and rating provides reliable product sales design mode for Amazon, and gives some sales suggestions, so as to enhance the product’s desirability. Research Article Data analysis of online shopping platform during the COVID-19 epidemic of Coronavirus disease Bin Zhao1* and Jinming Cao2 1School of Science, Hubei University of Technology, Wuhan, Hubei, China 2School of Information and Mathematics, Yangtze University, Jingzhou, Hubei, China Received: 31 October, 2020 Accepted: 25 January, 2021 Published: 28 January, 2021 *Corresponding authors: Dr. Bin Zhao, School of Science, Hubei University of Technology, Wuhan, Hubei, China, Tel/Fax: +86 13


Introduction
With the improvement of people's material life quality, more and more consumers have been favored high quality and high praise items, and become the fi rst choice of people's daily shopping. After purchasing, consumers grade the products through star rating and experience comments on the shopping website, and select high-quality products. The managers of shopping websites can understand the shortcomings of their products in other websites of the same type through consumer reviews, so as to grasp the advantages and disadvantages of product sales and clarify the development direction of products.
For consumers, the evaluation results can let them understand the specifi c ranking and advantages of different products of the same type, match with their own needs, so as to obtain better shopping. After purchasing the products, consumers also participate in the evaluation process of the goods, and as a group with the most say in the evaluation, they express their choices and wishes, so as to promote the businesses to fi nd themselves. As a result, consumers get a better shopping experience. These two groups are opposite to each other and promote each other, forming a virtuous circle.

The model of problems
The model of problem 1 Selection principle of indicators: The online shopping platform is complex. In order to get the product comment information scientifi cally and reasonably, the selection of indicators is a crucial step. Only when the indicators that can accurately measure the product are selected can the model be established [1], it should follow the following fi ve principles should in the selection process Figure 1. 2) Practical principle: the construction of the evaluation system is mainly theoretical analysis, which will be affected by the data sources of various indicators in practical application. Therefore, it should guarantee the availability and reliability of data sources in the process of selecting indicators. 3) The principle of system: there should be a certain logical relationship between the indicators, not a single index, but a system of product evaluation information, they should not only refl ect the sales and praise of the three products from different aspects, but also form a systematic organic whole.
4) Principle of comparability: different types of index data should conform to comparability, so the evaluation index system constructed conforms to universality.
5) The principle of relevance: three products selection of evaluation index system should be the combination of a series of indicators, in each of the products under the background of stars and comments, through the analysis of each product related comments properly and evaluation, not only can evaluate the effi cacy of the product of actual sales, but also can judge the trend of three kinds of product sales.
Based on the above selection principles of fi ve evaluation indexes, fi gure shows the overall framework of the comprehensive evaluation index system of the market products studied in this paper Figure 2.

Selection of evaluation indexes:
According to the scientifi c selection principle, combined with the relevant selection criteria of product evaluation body wash index [2] and the comments and sales of three products provided by sunshine company, the evaluation indexes that can better express the sales characteristics of products are selected as follows Table 1.

Credibility
Because credibility relates with many factors, when describing credibility, combined with the given data and conditions, the following three aspects describe the credibility: whether Amazon members are authenticated, whether products are confi rmed to be purchased and whether the number of votes is useful Figure 3.
In order to further analyze the infl uence of credibility and determine the infl uence weight of credibility, three factors affecting credibility are assigned and defi ned. The results are as follows Table 2.
Set useful voting as I i and useless voting as J i and obtain the functional relationship of the three products under the selected index with respect to reliability as follows: Where, F h is Credibility score ; a1, a2, a3 are the undetermined coeffi cient.
In order to select the indexes that have the greatest impact on the product evaluation system, the above four indexes were reduced by principal component analysis. The results are as follows:

Star rating
The company has designed two kinds of star rating for different objects, one is store feedback, the other is list, that is, product review. Customers can rate the store and products after purchasing goods to express the satisfaction of this shopping. After customer rating, the company will take the average number for the star rating given. Since the stars shown are the average number, half or most of the stars will appear.
The sales of the product will be judged by the star rating. The evaluation grade is 1-5 stars from low to high, which is directly converted into a judgment index. Finally, the sales situation of the products is determined by calculating the average stars. The evaluation grade is 1-5 stars from low to high, which is directly converted into a judgment index. Finally, the sales situation of the products is determined by calculating the average stars.
Indicators are classifi ed as follows Table 3.
The calculation method of star rating is as follows: Where F s is star rating;  I is the Number of stars ;  i is star index.

Comment title and comments
The comment Title often determines whether consumers are willing to continue reading and browsing the comment, so the importance of the title is obvious. When evaluating   In order to judge the evaluation index formed by the evaluation title more accurately, we will use the text length, Where a, b and C are the weight of text length, quantity of commodity characteristics and quantity of negative emotion words.
Model building: According to the three evaluation indexes selected above, a BP neural network model is constructed, and the process of establishing the model is shown in Figure 4.
Parameter setting of BP neural network model 1) Network layer number: Kolmogorov theorem [3][4][5] points out that in theory, three-layer neural network can fi t any continuous nonlinear function. In order to simplify the model, this paper uses three-layer neural network model.

2) Set input layer:
Four evaluation criteria are selected to describe the product, so the number of input layer neurons is 4.

4) Output layer setting:
The output result of the shopping evaluation model in this paper has only one comprehensive score about the product, so the output layer setting has only one neuron [5][6][7][8].

BP The solution of neural network evaluation model
Step 1: The connection weights between neurons in each layer of network initialization v ij , w jk , each weight value is assigned an interval random number in (-1,1), given calculation accuracy and maximum learning times M, give hidden layer threshold a j and output layer thresholds b k .
Step 2: Input sample Step 3: Hidden layer output calculation. According to the input vector X, connection weight between input layer and hidden layer V jk , and hidden layer threshold a, calculate hidden layer output.
Where m is number of hidden layer nodes v i0 = -1,x 0 = a j , f (.) is the implicit layer transfer function.
Step 4: Output layer output calculation. Output y according to the hidden layer, Connection weight w jk and threshold value b,Calculating the actual output O of BP neural network.
Step 6: Weight update. According to the overall network error E, update the network connection weight according to the Step 7: Training and convergence. When the average error of the calculated training sample is less than ε, the whole training is over, otherwise, the above process is repeated, and the weight and threshold are constantly modifi ed. After repeated calculation, the actual output of the network gradually approaches to the corresponding desired output, which is also the process of the global error of the network tending to the minimum. After repeated iterations, when the error is less than the allowable value, the training process of the network ends.

Conclusion of question 1
The principal component analysis is carried out to determine whether Amazon members have been certifi ed, whether purchasing power products and voting numbers have been confi rmed to be useful, and the evaluation index of credibility is obtained; the evaluation grade is taken as the second evaluation index, and the text length, the number of commodity characteristic words and the number of negative emotional words are mined with Spyder data, and the corresponding values are obtained after descriptive statistical analysis. And get the third evaluation index of the comment title. Then the evaluation model of three product evaluation indexes is established by using BP neural network.

The model of problem 2
In order to get the data measure that can best be tracked by the sunshine company from rating and comment, we choose star rating, helpful votes, total votes, and evaluation score as variables to establish four evaluation indexes. We use the fuzzy evaluation theory to discuss these indexes, and fi nally give their comprehensive impact to determine their fi nal data measure, The specifi c fl ow chart of the fuzzy evaluation theoretical model is shown in Figure 5.

Solution of model
Make correlation analysis between the comprehensive index data of sample data b j and the evaluation degree of sample data, and the results are shown in Table 7.
Conclusion: The analysis shows that the correlation between the two models is basically the same, which confi rms the accuracy of the neural network model of question one and this model. Through this model, we can accurately provide data measurement based on rating and comment for sunshine company, and sunshine company can analyze the market of goods according to these measurement.  According to the consumer's star level change and the overall change trend chart, we can know that the star level is on the rise. From 2013 to 2014, the rating rose rapidly, but it was also in a rapid decline stage in the same year, but the overall trend was still on the rise. In other words, the higher the star rating of consumers is, the higher the value is, the greater the reputation of products will be, and the greater the impact of consumers' purchase decisions will be.
In order to improve the analysis and the reliability of the analysis results, in view of this problem, the evaluation star level evaluation score is also considered comprehensively. After the correlation analysis, it is considered that the evaluation star level and the evaluation score are related to a certain extent, so     increase and decrease of product reputation in the online market when the data set of hair dryer is analyzed and discussed, the comprehensive index of star rating and evaluation score is directly considered in the analysis of microwave ovens, and the star to product is not considered separately Infl uence.
According to the data set of microwave oven, after preprocessing the missing value and time series, we can get the comprehensive trend chart of evaluation score and star level based on the measurement of time series as shown in Figure 11.
According to the comprehensive trend chart of microwave oven evaluation score and star rating based on time series measurement, the reputation of microwave oven is slowly decreasing in the online market at this stage.

Conclusion of question b
Based on the above analysis, it can be concluded that the reputation of hair dryer is increasing in the online market, the reputation of microwave oven is slowly decreasing, and the reputation of pacifi er is slowly increasing.

The model of problem c
Analysis of model: In order to better analysis of the product in a potential success and potential failure, we choose the most can refl ect real product quality indicators star-rating, evaluation score, number of comments, the fi ve-star rating proportion. See each item as a high-dimensional space of points, each evaluation index represents the dimension on this point, using the comprehensive evaluation method of fuzzy theory, the commodity properties of fuzzy similarity to high point, construct the fuzzy clustering model.

Establishment of model
Data selection: In order to avoid the volatility of evaluation indexes caused by too few data and ensure that the number of comments on each model is more than 20, we randomly select 20 products from three categories as samples and take the average value of sample indexes for analysis.
A total of 20 commodities are input into a two-dimensional matrix with respect to three variables, which is called the observation matrix: The column vector W i = (i=1,2,…20) represents the value of the evaluation variable corresponding to each commodity, and the row vector Y r (r = 1,2,3) represents the value of the sample data with respect to a certain evaluation variable.   Steps of fuzzy clustering model Step 1: Establish observation data matrix W for sample commodities; Step 2: The data matrix of the sample is standardized to unify the data structure. In this paper, the standard deviation change method is used to process: among them, W ave is the average value of four evaluation indexes of sample data,  is the standard deviation.
Step 3: There are many ways to calculate the relative distance of commodity between points in matrix space, such as Mahalanobis distance, absolute distance, Euclidean distance, etc.This paper adopts the most simple and practical European distance: Step 4: The average distance method and the shortest distance method can be used to calculate the distance between space classes.
Step 5: Through the sample matrix data, the above steps of fuzzy clustering model are carried out, and MATLAB is used to solve the problem, then the specifi c results can be obtained.

Solution of model
Because the scalar quantity we selected is positively related to the sales volume of the product, we select the largest data

The model of problem d and e
Index correlation analysis: In the sales of products in various aspects, star and review is the customer after the purchase of goods for the evaluation of the performance of the product, can objectively refl ect the product quality or not.
The amazon review the star rating of the basic principle is the addition of the positive and negative, and then according to the A9 algorithm weighted average, fi nally it is concluded that the shop star digital, has a certain scientifi c nature and accuracy.
In order to explore whether there is a certain infl uence relationship between star rating, text rating and product rating, the fi rst question is related to mining the selected text data, and the data is statistically analyzed to see whether there is a correlation between each star rating and review text and rating. Therefore, the text types are divided as follows: 1. The division of the fi ve star indexes is intuitively divided into fi ve weight standards according to the division standard in question one; 2. Through the word frequency statistics in the text comment, assign value weight to the characteristic words; 3. The number of valid votes is extracted as the score index of the product and the weight standard is obtained.
Pearson correlation analysis was carried out on the divided indexes, and the correlation results among the three product index ratings were obtained as follows: It can be seen from Tables 8-10 that the signifi cance between the stars, reviews and scores of the three products is P= 0.00<0.05, indicating that there is a linear relationship; the correlation between the three product evaluation indexes is greater than 0.7, indicating a high correlation, and the three products are closely related.

Answer to problem d and e
In order to determine the specifi c linear relationship between stars, reviews and ratings, the further relationship between the three indicators of each product was determined and the impact was predicted Figures 14-16.
Can be seen from the diagram, three star ratings, reviews and comments, there is an obvious linear relationship between the content of the comments and ratings with the increase of commodity star, tend to be more high praise, content and score also gradually rise, for those low star products, relatively, comments are more negative content, grading is low.
After evaluation of the products is an interconnected system, after customers to buy goods on the star rating, help review and product grade, interactions between the three, the size of the star indicators will affect the customer comments on the products and subsequent rating level. Star index is higher, affect the customer comments on the product content length, the more customers will write more positive emotional words, such as good, happy, and better, to express his love for the product, that matter, at the same time can also affect the customer to product good comments score, according to the customer's use of the products and comments are Face effects give them higher ratings.

Conclusion
This study is mainly based on the neural network model of Amazon Product sales strategy analysis.Through the screening and analysis of each index by BP neural network, fuzzy evaluation theory and time rating model, the market heat and Prospect of the three products are judged. It can be seen that higher star index will lead to more favorable comments and higher scores, and customers will write more enthusiastic words about good, well and excellent, It refl ects the sales volume of the products, and then provides the company with online sales strategy and affi rms the time-based mode. The text data helps the company to interact in the way of manufacturing successful products, which can also be used for the evaluation of Shopping platform quality, diagnosis and treatment of Shopping platform-related diseases.