CN112651768A - E-commerce analysis method and system based on block chain - Google Patents
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Abstract
The invention discloses an e-commerce analysis method and system based on a block chain. Classifying product comments according to different user geographic positions, segmenting words after preprocessing the product comments in different geographic positions, obtaining a keyword set by using a TF-IDF model, classifying the keyword set, carrying out dictionary matching on emotional tendencies to obtain classifications of different emotional tendencies, analyzing different emotional tendencies by an LDA topic model, and proposing suggestions for preventing user loss by combining the geographic positions of the users; and storing the classification and facies analysis results in the block chain, and updating the basic word bank in the block chain according to the classification results. The system comprises a web crawler module, an analysis module and a block chain storage module. According to the method, by increasing the geographical distribution of the users and the classification of the product comments, different sales strategies and after-sale services can be conveniently carried out on the users in different geographical positions by the merchants; by using the block chain, the safety and the privacy of data storage are improved.
Description
Technical Field
The invention relates to the technical field of e-commerce, in particular to an e-commerce analysis method and system based on a block chain.
Background
With the continuous development of the internet and the logistics industry, online shopping becomes the first choice of most people; meanwhile, the freedom degree of the netizens for publishing the information is high, and the propagation speed of the netizens is increased by geometric multiple. Based on the above, the product reviews after online shopping have great influence on the brand image and public praise of the product, the requirements and the preferences of the user can be known through the product reviews, the information is collected and analyzed, and brand service can be optimized, product iteration is accelerated, and the market occupation ratio can be presumed. However, the amount of data of the product review information is enormous, and it is very difficult to find useful information therefrom. The existing large e-commerce platforms often lack the functions of product comment classification and user geographic distribution analysis for analyzing commodity evaluation information, do not have the functions of product comment classification and user geographic distribution analysis, and cannot perform different sales strategies and after-sales services for users in different areas. Meanwhile, in data storage, the existing security and privacy are poor, and data cannot be stored safely.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a block chain-based e-commerce analysis method and system which can improve the safety and privacy of data storage, classify product reviews and analyze user geographical distribution.
In order to solve the technical problem, the invention provides an e-commerce analysis method based on a block chain, which comprises the following steps:
step 1: obtaining product comment information and corresponding user geographical position information, and classifying the product comment information according to different user geographical positions;
step 2: product comment information in different geographic positions is preprocessed, and word segmentation is carried out on preprocessed data to obtain a word set;
and step 3: carrying out data mining on the word set by using a TF-IDF model, and removing non-key words to obtain a keyword set;
and 4, step 4: classifying the keyword set, and performing dictionary matching of emotional tendency on the classified keywords to obtain classification of different emotional tendency;
and 5: analyzing different emotional tendencies through an LDA topic model, analyzing the reason of user loss and providing a suggestion for preventing the user loss by combining the geographical position of the user;
step 6: and classifying the emotional tendency in different geographic positions and storing corresponding analysis results in the block chain, and updating the basic word bank in the block chain according to the classification results of different emotional tendency.
Further, the product comment information is preprocessed in the step 2, so that the comment information is deduplicated.
Further, in the step 2, the preprocessed data is segmented, and a segmentation method is adopted by using a jieba word segmentation device.
Further, in the step 3, a TF-IDF model is used to perform data mining on the word set, specifically, a word list to be removed is defined, a skleran module under Python is used to implement TF-IDF, and a keyword set suitable for classification is selected.
Further, the method for classifying the keyword set in step 4 is to perform clustering by using k-means, specifically, perform k-means clustering on the keyword set to obtain a classification and a centroid of each class, calculate a distance from each keyword to the centroid, select a class closest to the centroid, and add the class.
Further, in the step 5, different emotional tendencies are analyzed through the LDA topic model, specifically, potential topics in the data set are mined through the LDA topic model, and then concentrated attention points and related feature words of the data set are analyzed, and the reason for user loss is analyzed on the basis.
The invention also provides an e-commerce analysis system based on the block chain, which is characterized in that: comprises a web crawler module, an analysis module and a block chain storage module,
the web crawler module is used for acquiring product comment information and corresponding user geographical position information, providing the information to the analysis module and updating the information at regular time;
the analysis module analyzes the reason of the user loss and provides a suggestion for preventing the user loss by combining the geographical position of the user;
the block chain storage module is used for storing the classification of different emotional tendencies and geographic positions and corresponding analysis results on the block chain.
Further, the analysis module comprises a geographic classification module, a comment classification module and an attrition prediction module,
the geographic classification module classifies the information of the geographic positions of the users extracted by the web crawler module and provides the comment classification module with the comment information of the products in different geographic positions;
the comment classification module classifies the product comment information in different geographic positions to obtain classifications of different emotional tendencies and provides classification results to the loss prediction module;
the loss prediction module analyzes different emotional tendencies and provides suggestions for preventing user loss by combining the geographical position of the user.
Furthermore, the comment classification module performs word segmentation on the product comment information in different geographic positions, performs data mining on a word set by using a TF-IDF model on the basis of the word segmentation to obtain a keyword set, and performs dictionary matching of emotional tendency after the keyword set is classified to obtain classification of different emotional tendency.
Further, the geographic classification module generates a corresponding distribution graph according to different user geographic position information, and associates the distribution graph with comment data to search local user comment information according to the geographic position.
The invention has the beneficial effects that: according to the method, the geographical distribution of the users and the classification of the product comments are increased, the reason of the user loss can be analyzed, the suggestion for preventing the user loss is provided by combining the geographical positions of the users, and different sales strategies and after-sale services can be conveniently carried out on the users in different geographical positions by merchants; by using blockchain storage, the security and the privacy of data storage are improved.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical solutions of the present invention more clearly understood and to implement them in accordance with the contents of the description, the following detailed description is given with reference to the preferred embodiments of the present invention and the accompanying drawings.
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Fig. 1 is a flow chart of a block chain based e-commerce analysis method.
Fig. 2 is a block chain-based framework diagram of an e-commerce analysis system.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
In the description of the present invention, it should be understood that the term "comprises/comprising" is intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As shown in the flowchart of fig. 1, the block chain-based e-commerce analysis method of the present invention includes the following steps:
step 1: and obtaining the product comment information and the corresponding user geographical position information, and classifying the product comment information according to different user geographical positions. In this embodiment, product review information is preprocessed to remove duplicate comment information. If the comment information of different shoppers is completely repeated, the probability is that a single line is swiped, the information is generally meaningless, so that the completely repeated comment information needs to be deduplicated, and useful comment information and corresponding user geographical position information are reserved.
Step 2: product comment information in different geographic positions is preprocessed, and word segmentation is carried out on preprocessed data to obtain a word set. In this embodiment, the preprocessed data is subjected to word segmentation, and the word segmentation method is implemented by using a jieba word segmentation device.
And step 3: and (4) carrying out data mining on the word set by using a TF-IDF model, and removing non-key words to obtain a keyword set. Defining a word list needing to be removed, using a sklern module under Python to realize TF-IDF, and selecting a keyword set suitable for classification. In this embodiment, the word list to be removed is defined to include english, numbers, panning, down jackets, and the like, and then the tfidern module under Python is used to implement TF-IDF, that is, the tfidfvactor function provided by the skleern is used to create the object of the word steering amount, and the fit _ transform function converts the keyword list into the word frequency matrix. TF-IDF (term frequency-inverse document frequency) is a common weighting technology for information retrieval and data mining, is commonly used for mining keywords in articles, has simple and efficient algorithm, and is often used for text data cleaning. If a word or a word appears in an article with a high frequency TF and rarely appears in other articles, the word or the word is considered to have a good classification capability and is suitable for classification. Wherein TF represents the frequency of occurrences of terms in the article; IDF represents the word discrimination, which is greater if there are fewer documents containing a word.
And 4, step 4: classifying the keyword set, in this embodiment, classifying the keyword set by using k-means, specifically, performing k-means clustering on the keyword set to obtain a classification and a centroid of each type, then calculating a distance from each keyword to the centroid, and selecting the closest class to add to complete classification. The method for classifying the keyword set can also be used for enabling the words classified by the jieba word classifier to collide with a basic word stock stored in the block chain and classifying the words into corresponding classifications. The word frequency statistics is carried out on various keywords, word cloud is drawn to check word segmentation effects, the word cloud provides a certain degree of first impression, the attention can be attracted compared with other bar graphs, the word frequency of texts or websites can be analyzed rapidly, and the texts or the websites can be displayed in various styles. Performing dictionary matching of emotional tendency on the classified keywords to obtain classification of different emotional tendency; the emotional tendency is the attitude expressed by the user to the commodity, namely positive emotion, negative emotion and neutral emotion. The dictionary matching method used in this embodiment is a word set (beta version) for web-emotion analysis, which can better process the product comment information and perform corresponding optimization on the word set. Words frequently used on online shopping comments such as high cost performance, fast logistics, good service and the like are added into the positive emotion word list according to the emotional tendency to obtain three types of positive emotion, negative emotion and neutral emotion.
And 5: different emotional tendencies are analyzed through an LDA topic model, the reason of user loss is analyzed, and a suggestion for preventing the user loss is provided by combining the geographical position of the user. Potential subjects in the data set are mined through the LDA subject model, concentrated attention points and relevant feature words of the data set are further analyzed, and the reason of user loss is analyzed on the basis. The topics under different emotional tendencies and the high-frequency characteristic words in the topics are mined, the concentrated attention points and the related characteristic words are found, namely, for key indexes of the lost users, such as cost performance, logistics, service and the like, a basic basis is provided for analyzing the reason of the user loss according to different indexes, and suggestions for preventing the user loss are provided according to the basic basis. And (3) constructing LDA theme models for positive emotion, negative emotion and neutral emotion respectively by using Gensim of Python, analyzing the themes and high-frequency feature words in the themes to obtain the reason for user churn, and proposing suggestions on how to increase user viscosity and prevent user churn of the product according to the geographical position of the user. For example, the overall quality of customer service and the quality of service can be improved according to the after-sales service index. Words and probabilities obtained by LDA theme analysis on negative emotions are the basic reason of user loss, while LDA theme analysis on positive emotions is the basic reason of product purchase by users, and suggestion is provided to keep current service.
Step 6: and classifying the emotional tendency in different geographic positions and storing corresponding analysis results in the block chain, and updating the basic word bank in the block chain according to the classification results of different emotional tendency.
As shown in the frame diagram of fig. 2, the block chain-based e-commerce analysis system of the present invention includes a web crawler module, an analysis module, and a block chain storage module. The web crawler module is used for acquiring product comment information and corresponding user geographical position information, providing the information to the analysis module and updating the information at regular time; the analysis module analyzes the reason of the user loss and provides a suggestion for preventing the user loss by combining the geographical position of the user; the block chain storage module is used for storing the classification of different emotional tendencies and geographic positions and corresponding analysis results on the block chain, and the storage results on the block chain are used for improving the safety and the privacy of data storage. Blockchain storage is a decentralized and data-sharing storage system constructed by using blockchains, and is an effective combination of blockchains and storage systems. The data stored in the storage device has the characteristics of being incapable of being forged, having trace in the whole process, being traceable, being publicly transparent, being maintained in a collective mode and the like. The information stored on the blockchain is public, but the account identity information is highly encrypted and can only be accessed under the authorization of the data owner, thereby ensuring the security of the data and the privacy of individuals.
In this embodiment, the analysis module includes a geo-classification module, a review classification module, and an attrition prediction module. The geographic classification module classifies the information of the geographic positions of the users extracted by the web crawler module and provides the comment classification module with the comment information of the products in different geographic positions; the comment classification module classifies the product comment information in different geographic positions to obtain classifications of different emotional tendencies and provides classification results to the loss prediction module; the loss prediction module analyzes different emotional tendencies and provides suggestions for preventing user loss by combining the geographical position of the user.
In this embodiment, the comment classification module performs word segmentation on product comment information in different geographic positions, performs data mining on a word set by using a TF-IDF model on the basis of the word segmentation to obtain a keyword set, and performs dictionary matching of emotional tendencies after classifying the keyword set to obtain classifications of different emotional tendencies.
In this embodiment, the geo-categorizing module generates a corresponding distribution graph according to different user geographical location information, associates the distribution graph with comment data, and is configured to search local user comment information according to a geographical location, and input a place name to obtain corresponding local user comment information. Through the distribution diagram, the merchant can check and analyze the comments of all merchants corresponding to the products, and for the merchants, the merchant can visually compare the advantages and disadvantages of the merchants with those of the same party to find out the places needing improvement.
The invention has the beneficial effects that: according to the method, the geographical distribution of the users and the classification of the product comments are increased, the reason of the user loss can be analyzed, the suggestion for preventing the user loss is provided by combining the geographical positions of the users, and different sales strategies and after-sale services can be conveniently carried out on the users in different geographical positions by merchants; by using blockchain storage, the security and the privacy of data storage are improved.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (10)
1. An e-commerce analysis method based on a block chain is characterized by comprising the following steps:
step 1: obtaining product comment information and corresponding user geographical position information, and classifying the product comment information according to different user geographical positions;
step 2: product comment information in different geographic positions is preprocessed, and word segmentation is carried out on preprocessed data to obtain a word set;
and step 3: carrying out data mining on the word set by using a TF-IDF model, and removing non-key words to obtain a keyword set;
and 4, step 4: classifying the keyword set, and performing dictionary matching of emotional tendency on the classified keywords to obtain classification of different emotional tendency;
and 5: analyzing different emotional tendencies through an LDA topic model, analyzing the reason of user loss and providing a suggestion for preventing the user loss by combining the geographical position of the user;
step 6: and classifying the emotional tendency in different geographic positions and storing corresponding analysis results in the block chain, and updating the basic word bank in the block chain according to the classification results of different emotional tendency.
2. The block chain-based e-commerce analysis method of claim 1, wherein: and in the step 2, product comment information is preprocessed to remove duplication of the comment information.
3. The block chain-based e-commerce analysis method of claim 1, wherein: and 2, performing word segmentation on the preprocessed data in the step 2, wherein a word segmentation method is adopted by using a jieba word segmentation device.
4. The block chain-based e-commerce analysis method of claim 1, wherein: and 3, performing data mining on the word set by using a TF-IDF model, specifically defining a word list to be removed, realizing TF-IDF by using a sklern module under Python, and selecting a keyword set suitable for classification.
5. The block chain-based e-commerce analysis method of claim 1, wherein: the method for classifying the keyword set in the step 4 is to perform clustering by adopting k-means, specifically to perform k-means clustering on the keyword set to obtain a classification and a centroid of each type, then calculate the distance from each keyword to the centroid, select the closest class to add, and complete classification.
6. The block chain-based e-commerce analysis method of claim 1, wherein: in the step 5, different emotional tendencies are analyzed through the LDA topic model, specifically, potential topics in the data set are mined through the LDA topic model, concentrated attention points and relevant feature words of the data set are further analyzed, and the reason of user loss is analyzed on the basis.
7. An e-commerce analysis system based on a block chain is characterized in that: comprises a web crawler module, an analysis module and a block chain storage module,
the web crawler module is used for acquiring product comment information and corresponding user geographical position information, providing the information to the analysis module and updating the information at regular time;
the analysis module analyzes the reason of the user loss and provides a suggestion for preventing the user loss by combining the geographical position of the user;
the block chain storage module is used for storing the classification of different emotional tendencies and geographic positions and corresponding analysis results on the block chain.
8. The block chain-based e-commerce analysis system of claim 7, wherein: the analysis module comprises a geographical classification module, a comment classification module and an attrition prediction module,
the geographic classification module classifies the information of the geographic positions of the users extracted by the web crawler module and provides the comment classification module with the comment information of the products in different geographic positions;
the comment classification module classifies the product comment information in different geographic positions to obtain classifications of different emotional tendencies and provides classification results to the loss prediction module;
the loss prediction module analyzes different emotional tendencies and provides suggestions for preventing user loss by combining the geographical position of the user.
9. The block chain-based e-commerce analysis system of claim 8, wherein: the comment classification module is used for segmenting the product comment information in different geographic positions, on the basis, a TF-IDF model is used for carrying out data mining on a word set to obtain a keyword set, and after the keyword set is classified, dictionary matching of emotional tendency is carried out to obtain classification of different emotional tendency.
10. The block chain-based e-commerce analysis system of claim 8, wherein: the geographic classification module generates corresponding distribution graphs according to different user geographic position information, and associates the distribution graphs with comment data to search local user comment information according to geographic positions.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113450153A (en) * | 2021-07-02 | 2021-09-28 | 京东科技控股股份有限公司 | Data processing method and device |
| CN115795040A (en) * | 2023-02-10 | 2023-03-14 | 成都桉尼维尔信息科技有限公司 | User portrait analysis method and system |
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