CN110929123A - E-commerce product competition analysis method and system - Google Patents
E-commerce product competition analysis method and system Download PDFInfo
- Publication number
- CN110929123A CN110929123A CN201910968980.7A CN201910968980A CN110929123A CN 110929123 A CN110929123 A CN 110929123A CN 201910968980 A CN201910968980 A CN 201910968980A CN 110929123 A CN110929123 A CN 110929123A
- Authority
- CN
- China
- Prior art keywords
- commerce
- product
- emotion
- consumer
- product attribute
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/951—Indexing; Web crawling techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/33—Querying
- G06F16/3331—Query processing
- G06F16/3332—Query translation
- G06F16/3335—Syntactic pre-processing, e.g. stopword elimination, stemming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
Abstract
The invention provides an E-commerce product competition analysis method and system, wherein the method comprises the following steps: establishing an E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data; mining each product attribute of the E-commerce product; determining the emotion polarity of each product attribute of a consumer according to the E-commerce comment emotion dictionary; determining a weight of each product attribute based on the frequency of occurrence of each product attribute; and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute. The method processes and analyzes the E-commerce comment data, calculates the overall emotion of the consumer to each E-commerce product, analyzes the competitive capacity of each E-commerce product according to the overall emotion of the consumer to each E-commerce product, and provides valuable information for E-commerce product merchants and consumers.
Description
Technical Field
The invention belongs to the technical field of electronic commerce, and particularly relates to a method and a system for analyzing E-commerce product competition.
Background
With the rapid development of electronic commerce, online shopping is becoming an important shopping mode. E-commerce reviews, i.e., consumer voices, play an important role in product competition, and traditional methods in this field have focused primarily on market research and questionnaires to obtain customer preferences.
E-commerce reviews, however, provide a good and reliable channel to not only understand consumer demand for a product or service, but also to analyze product competition in the marketplace. With the increasing number of online shopping crowds, e-commerce comment data shows explosive growth, wherein the number of comments on a product or a service can reach thousands, tens of thousands or even millions, and for potential consumers and merchants, reading one by one is obviously impossible, and one-sided results can be obtained by only looking at one part.
Disclosure of Invention
To overcome the above existing problems or at least partially solve the above problems, embodiments of the present invention provide a method and system for analyzing the competition of e-commerce products.
According to a first aspect of the embodiments of the present invention, there is provided an e-commerce product competition analysis method, including:
establishing an E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data;
mining each product attribute of the E-commerce products according to the E-commerce comment data;
determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary;
determining a weight of each product attribute based on the frequency of occurrence of each product attribute;
and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the establishing of the E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data comprises:
preprocessing each piece of E-commerce comment data, and establishing an E-commerce comment corpus;
and establishing an E-commerce comment emotion dictionary based on a hownet dictionary according to the E-commerce comment corpus.
Further, the preprocessing comprises word segmentation, stop word removal and part-of-speech tagging.
Further, the mining each product attribute of the e-commerce product according to the plurality of pieces of e-commerce comment data includes:
and mining a theme in each piece of E-commerce comment data by adopting an LDA theme model, wherein the theme is a product attribute, and obtaining a plurality of product attributes corresponding to the plurality of pieces of E-commerce comment data.
Further, the consumer's emotional polarity for each product attribute includes a positive emotional polarity, a negative emotional polarity, and a neutral emotional polarity.
Further, the determining the weight of each product attribute based on the frequency of occurrence of each product attribute comprises:
and counting the frequency of each product attribute in the E-commerce comment data, and determining the weight of each product attribute according to the frequency.
Further, the calculating the overall emotion of the consumer on the E-commerce product according to the emotion polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute comprises the following steps:
assigning the emotion polarity of each product attribute;
and calculating the overall emotion of the consumer on the E-commerce product according to the evaluation of the emotion polarity of each product attribute and the weight of each product attribute.
Further, the method also comprises the following steps:
for any E-commerce product, a graph is drawn between the E-commerce product and the overall emotion of the consumer on the E-commerce product, and the competitive power of the E-commerce product is analyzed.
According to a second aspect of the embodiments of the present invention, there is provided an e-commerce product competition analysis system, including:
the crawling module is used for establishing an E-commerce comment emotion dictionary based on the crawled plurality of E-commerce comment data;
the mining module is used for mining each product attribute of the E-commerce products according to the E-commerce comment data;
the first determining module is used for determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary;
a second determining module for determining a weight of each product attribute based on a frequency of occurrence of each product attribute;
and the calculating module is used for calculating the total emotion of the consumer on the E-commerce product according to the emotion polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
According to a third aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor calls the program instructions to perform the method for analyzing product competition of electronic commerce provided in any one of the various possible implementations of the first aspect.
According to a fourth aspect of the embodiments of the present invention, there is also provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for analyzing competition of electric commerce products provided in any one of the various possible implementations of the first aspect.
The embodiment of the invention provides an e-commerce product competition analysis method and system, which are used for processing and analyzing e-commerce comment data, calculating the overall emotion of a consumer on each e-commerce product, and analyzing the competitive power of each e-commerce product according to the overall emotion of the consumer on each e-commerce product, so that valuable information is provided for merchants and consumers of the e-commerce products.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic overall flow chart of an e-commerce product competition analysis method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for establishing an E-commerce comment sentiment dictionary according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an overall structure of an E-commerce product competition analysis system according to an embodiment of the present invention;
fig. 4 is a schematic view of an overall structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
In an embodiment of the present invention, a method for analyzing e-commerce product competition is provided, and fig. 1 is a schematic overall flow chart of the method for analyzing e-commerce product competition provided by the embodiment of the present invention, where the method includes: establishing an E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data; mining each product attribute of the E-commerce products according to the E-commerce comment data; determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary; determining a weight of each product attribute based on the frequency of occurrence of each product attribute; and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
It can be understood that after the consumer purchases the e-commerce product on the e-commerce platform, the consumer can comment on the purchased e-commerce product, and according to the comment on the e-commerce product by the consumer, the emotion of the consumer on the purchased e-commerce product can be known. The embodiment of the invention crawls the E-commerce comment data on the E-commerce platform, namely, crawls a plurality of comment data of each E-commerce product (hereinafter referred to as a plurality of E-commerce comment data of each E-commerce product), processes and analyzes the E-commerce comment data, calculates the overall emotion of a consumer to each E-commerce product, analyzes the competitive power of each E-commerce product according to the overall emotion of the consumer to each E-commerce product, and uses an automatic calculation method (such as theme analysis and emotion analysis) to process a large amount of comment data for a plurality of E-commerce comment data to extract meaningful information and insight, thereby providing valuable information for E-commerce product merchants and consumers.
Referring to fig. 2, on the basis of the above embodiment, in the embodiment of the present invention, the establishing an e-commerce comment emotion dictionary based on the crawled multiple pieces of e-commerce comment data includes:
preprocessing each piece of E-commerce comment data, and establishing an E-commerce comment corpus;
and establishing an E-commerce comment emotion dictionary based on a hownet dictionary according to the E-commerce comment corpus.
It can be understood that for each piece of crawled e-commerce comment data (including multiple pieces of e-commerce comment data of multiple e-commerce products), an e-commerce comment corpus is established, and particularly when the e-commerce comment corpus is established, an e-commerce comment emotion dictionary is established based on a hownet dictionary. The method comprises the steps of preprocessing each piece of E-commerce comment data, carrying out word segmentation, stop word removal and part-of-speech tagging on each piece of E-commerce comment data, and establishing an E-commerce comment corpus according to words obtained after preprocessing.
On the basis of the above embodiments, in the embodiment of the present invention, mining each product attribute of an e-commerce product according to a plurality of pieces of e-commerce comment data includes:
and mining a theme in each piece of E-commerce comment data by adopting an LDA theme model, wherein the theme is a product attribute, and obtaining a plurality of product attributes corresponding to the plurality of pieces of E-commerce comment data.
It can be understood that, for a plurality of pieces of e-commerce comment data of each e-commerce product, a plurality of product attributes of each e-commerce product are mined from the e-commerce comment data, specifically, a topic in each e-commerce comment data is mined by using an LDA topic model, where the topic is a product attribute. The lda (latent Dirichlet allocation) is a document topic generation model, which is also called a three-layer bayesian probability model, and includes three layers of structures, i.e., words, topics, and documents. The generative model is a process in which each word of an article is considered to be obtained by "selecting a topic with a certain probability and selecting a word from the topic with a certain probability". Then for a plurality of pieces of comment data of each e-commerce product, a plurality of product attributes can be mined from the comment data, namely, a plurality of product attributes correspond to each e-commerce product.
Based on the above embodiments, in the embodiment of the present invention, the emotional polarities of the consumers for each product attribute include a positive emotional polarity, a negative emotional polarity, and a neutral emotional polarity.
It can be understood that the e-commerce comment sentiment dictionary established according to the above embodiment determines the sentiment polarity of the consumer for each product attribute in each piece of e-commerce comment data, and in particular, for a plurality of pieces of e-commerce comment data of any one e-commerce product, the sentiment polarity of the consumer for each product attribute of any one e-commerce product is analyzed from each piece of e-commerce comment data. The emotion polarities in the embodiment of the present invention mainly include a positive emotion polarity, a negative emotion polarity, and a neutral emotion polarity.
On the basis of the above embodiment, in the embodiment of the present invention, determining the weight of each product attribute based on the frequency of occurrence of each product attribute includes:
and counting the frequency of each product attribute in the E-commerce comment data, and determining the weight of each product attribute according to the frequency.
It can be understood that, for a plurality of pieces of e-commerce comment data of one e-commerce product, at least one product attribute of the e-commerce product can be mined from any one piece of comment data, and for a plurality of pieces of e-commerce comment data, a plurality of product attributes can be mined from the plurality of pieces of e-commerce comment data, and for the same product attribute, the plurality of product attributes may appear for many times. The embodiment of the invention determines the weight of each product attribute according to the frequency of occurrence of each product attribute.
On the basis of the above embodiment, in the embodiment of the present invention, the calculating the overall emotion of the consumer on the e-commerce product according to the emotional polarity of the consumer on each product attribute of the e-commerce product and the weight of each product attribute includes:
assigning the emotion polarity of each product attribute;
and calculating the overall emotion of the consumer on the E-commerce product according to the evaluation of the emotion polarity of each product attribute and the weight of each product attribute.
It will be appreciated that while the above embodiments determine the emotional polarity of the consumer for each product attribute of the e-commerce product, embodiments of the present invention assign different values to different emotional polarities, such as assigning a value of 1 to a positive emotional polarity, assigning a value of-1 to a negative emotional polarity, and assigning a value of 0 to a neutral emotional polarity. And finally, calculating the total emotion of the consumer on the E-commerce product according to the emotion polarity assignment of each product attribute and the weight of each product attribute.
On the basis of the above embodiments, the embodiments of the present invention further include: for any E-commerce product, a graph is drawn between the E-commerce product and the overall emotion of the consumer on the E-commerce product, and the competitive power of the E-commerce product is analyzed.
The total emotion of the consumer for each e-commerce product is obtained through calculation through the embodiment, for each e-commerce product, a chart between each e-commerce product and the total emotion of the consumer for the e-commerce product is drawn, and the competitive power of each e-commerce product can be analyzed. For example, in the above embodiment, when the consumer has a higher overall emotional score for a certain e-commerce product, it indicates that the e-commerce product has a higher competitive power.
In another embodiment of the present invention, an e-commerce product competition analysis system is provided, which is used for implementing the method in the foregoing embodiments. Therefore, the descriptions and definitions in the embodiments of the foregoing e-commerce product competition analysis method can be used for understanding the execution modules in the embodiments of the present invention. Fig. 3 is a schematic diagram of an overall structure of an e-commerce product competition analyzing system according to an embodiment of the present invention, where the system includes a crawling module 31, a mining module 32, a first determining module 33, a second determining module 34, and a calculating module 35.
The crawling module 31 is used for establishing an E-commerce comment emotion dictionary based on a plurality of pieces of crawled E-commerce comment data;
the mining module 32 is used for mining each product attribute of the E-commerce products according to the E-commerce comment data;
the first determining module 33 is configured to determine, according to the e-commerce comment emotion dictionary, emotion polarities of consumers in each piece of e-commerce comment data on each product attribute;
a second determining module 34 for determining a weight for each product attribute based on the frequency of occurrence of each product attribute;
and the calculating module 35 is used for calculating the overall emotion of the consumer on the E-commerce product according to the emotion polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
The e-commerce product competition analysis system provided by the embodiment of the invention corresponds to the e-commerce product competition analysis method provided by each embodiment, and the relevant technical features of the e-commerce product competition analysis system can refer to the relevant technical features of the e-commerce product competition analysis method provided by the embodiment, and are not described herein again.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)01, a communication interface (communication interface)02, a memory (memory)03 and a communication bus 04, wherein the processor 01, the communication interface 02 and the memory 03 complete communication with each other through the communication bus 04, and the processor 01 can call logic instructions in the memory 03 to execute the following method: establishing an E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data; mining each product attribute of the E-commerce products according to the E-commerce comment data; determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary; determining a weight of each product attribute based on the frequency of occurrence of each product attribute; and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
In addition, the logic instructions in the memory 03 can be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The present embodiments provide a non-transitory computer-readable storage medium storing computer instructions that cause a computer to perform the methods provided by the above method embodiments, for example, including: establishing an E-commerce comment emotion dictionary based on the plurality of pieces of crawled E-commerce comment data; mining each product attribute of the E-commerce products according to the E-commerce comment data; determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary; determining a weight of each product attribute based on the frequency of occurrence of each product attribute; and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
The embodiment of the invention provides an e-commerce product competition analysis method and system, wherein e-commerce comment data on an e-commerce platform are crawled, namely a plurality of comment data of each e-commerce product (hereinafter referred to as a plurality of e-commerce comment data of each e-commerce product) are crawled, the e-commerce comment data are processed and analyzed, the overall emotion of a consumer on each e-commerce product is calculated, the competition capability of the consumer on each e-commerce product is analyzed according to the overall emotion of the consumer on each e-commerce product, and for a plurality of e-commerce comment data, a large amount of comment data are processed by using an automatic calculation method (such as theme analysis and emotion analysis) to extract meaningful information and insights and provide valuable information for e-commerce product merchants and consumers.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the method embodiments may be implemented by hardware related to program instructions, and the program may be stored in a computer readable storage medium, and when executed, the program performs the steps including the method embodiments; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An e-commerce product competition analysis method, comprising:
establishing an E-commerce comment emotion dictionary based on a plurality of pieces of E-commerce comment data of the crawled E-commerce products;
mining each product attribute of the E-commerce products according to the E-commerce comment data;
determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary;
determining a weight of each product attribute based on the frequency of occurrence of each product attribute;
and calculating the overall emotion of the consumer on the E-commerce product according to the emotional polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
2. The e-commerce product competition analysis method of claim 1, wherein the building of the e-commerce comment sentiment dictionary based on the crawled plurality of pieces of e-commerce comment data comprises:
preprocessing each piece of E-commerce comment data, and establishing an E-commerce comment corpus;
and establishing an E-commerce comment emotion dictionary based on a hownet dictionary according to the E-commerce comment corpus.
3. The e-commerce product competition analysis method of claim 2, wherein the preprocessing comprises word segmentation, stop word and part-of-speech tagging.
4. The e-commerce product competition analysis method of claim 2, wherein the mining each product attribute of the e-commerce product according to the plurality of pieces of e-commerce comment data comprises:
and mining a theme in each piece of E-commerce comment data by adopting an LDA theme model, wherein the theme is a product attribute, and obtaining a plurality of product attributes corresponding to the plurality of pieces of E-commerce comment data.
5. The e-commerce product competition analysis method of claim 1, wherein the consumer sentiment polarity for each product attribute includes a positive sentiment polarity, a negative sentiment polarity, and a neutral sentiment polarity.
6. The e-commerce product competition analysis method of claim 5, wherein determining the weight of each product attribute based on the frequency of occurrence of each product attribute comprises:
and counting the frequency of each product attribute in the E-commerce comment data, and determining the weight of each product attribute according to the frequency.
7. The e-commerce product competition analysis method of claim 1, wherein calculating the overall consumer emotion to the e-commerce product according to the consumer emotion polarity to each product attribute and the weight of each product attribute of the e-commerce product comprises:
assigning the emotion polarity of each product attribute;
and calculating the overall emotion of the consumer on the E-commerce product according to the emotion polarity assignment of each product attribute and the weight of each product attribute.
8. The e-commerce product competition analysis method of claim 7, further comprising:
for any E-commerce product, a graph is drawn between the E-commerce product and the overall emotion of the consumer on the E-commerce product, and the competitive power of the E-commerce product is analyzed.
9. An e-commerce product competition analysis system, comprising:
the crawling module is used for establishing an E-commerce comment emotion dictionary based on the crawled plurality of E-commerce comment data;
the mining module is used for mining each product attribute of the E-commerce products according to the E-commerce comment data;
the first determining module is used for determining the emotion polarity of each product attribute of the consumer in each piece of E-commerce comment data according to the E-commerce comment emotion dictionary;
a second determining module for determining a weight of each product attribute based on a frequency of occurrence of each product attribute;
and the calculating module is used for calculating the total emotion of the consumer on the E-commerce product according to the emotion polarity of the consumer on each product attribute of the E-commerce product and the weight of each product attribute.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the e-commerce product competition analysis method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910968980.7A CN110929123A (en) | 2019-10-12 | 2019-10-12 | E-commerce product competition analysis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910968980.7A CN110929123A (en) | 2019-10-12 | 2019-10-12 | E-commerce product competition analysis method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110929123A true CN110929123A (en) | 2020-03-27 |
Family
ID=69848868
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910968980.7A Pending CN110929123A (en) | 2019-10-12 | 2019-10-12 | E-commerce product competition analysis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110929123A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905740A (en) * | 2021-02-04 | 2021-06-04 | 合肥工业大学 | Topic preference mining method for competitive product hierarchy |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100169317A1 (en) * | 2008-12-31 | 2010-07-01 | Microsoft Corporation | Product or Service Review Summarization Using Attributes |
| CN106649260A (en) * | 2016-10-19 | 2017-05-10 | 中国计量大学 | Product feature structure tree construction method based on comment text mining |
| CN108536654A (en) * | 2018-04-13 | 2018-09-14 | 科大讯飞股份有限公司 | Identify textual presentation method and device |
| CN109101478A (en) * | 2018-06-04 | 2018-12-28 | 东南大学 | A kind of Aspect grade sentiment analysis method towards electric business comment text |
-
2019
- 2019-10-12 CN CN201910968980.7A patent/CN110929123A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100169317A1 (en) * | 2008-12-31 | 2010-07-01 | Microsoft Corporation | Product or Service Review Summarization Using Attributes |
| CN106649260A (en) * | 2016-10-19 | 2017-05-10 | 中国计量大学 | Product feature structure tree construction method based on comment text mining |
| CN108536654A (en) * | 2018-04-13 | 2018-09-14 | 科大讯飞股份有限公司 | Identify textual presentation method and device |
| CN109101478A (en) * | 2018-06-04 | 2018-12-28 | 东南大学 | A kind of Aspect grade sentiment analysis method towards electric business comment text |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112905740A (en) * | 2021-02-04 | 2021-06-04 | 合肥工业大学 | Topic preference mining method for competitive product hierarchy |
| CN112905740B (en) * | 2021-02-04 | 2022-08-30 | 合肥工业大学 | Topic preference mining method for competitive product hierarchy |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107391493B (en) | Public opinion information extraction method and device, terminal equipment and storage medium | |
| US10282737B2 (en) | Analyzing sentiment in product reviews | |
| Htay et al. | Extracting product features and opinion words using pattern knowledge in customer reviews | |
| EP3413221A1 (en) | Risk assessment method and system | |
| US20180374141A1 (en) | Information pushing method and system | |
| CN111914086A (en) | Method and system for analyzing mobile phone comments based on LSTM neural network | |
| JP2019519019A (en) | Method, apparatus and device for identifying text type | |
| CN112733042A (en) | Recommendation information generation method, related device and computer program product | |
| CN108256098A (en) | A kind of method and device of determining user comment Sentiment orientation | |
| CN110913354A (en) | Short message classification method and device and electronic equipment | |
| CN110706028A (en) | Commodity evaluation emotion analysis system based on attribute characteristics | |
| CN106919997B (en) | LDA-based user consumption prediction method for electronic commerce | |
| CN111966886A (en) | Object recommendation method, object recommendation device, electronic equipment and storage medium | |
| CN112149003A (en) | Commodity community recommendation method and device and computer equipment | |
| Shan et al. | Inconsistency investigation between online review content and ratings | |
| Daniati et al. | Decision Making Framework Based On Sentiment Analysis in Twitter Using SAW and Machine Learning Approach | |
| CN110929123A (en) | E-commerce product competition analysis method and system | |
| CN112330373A (en) | User behavior analysis method and device and computer readable storage medium | |
| WO2020057237A1 (en) | Influence detection method applicable to object of interest, and electronic terminal and storage medium | |
| CN110019702B (en) | Data mining method, device and equipment | |
| Prihono et al. | Comparison analysis of social influence marketing for mobile payment using support vector machine | |
| CN110795537B (en) | Method, device, equipment and medium for determining improvement strategy of target commodity | |
| Zhang et al. | Discovering Social Media Timing Signals for Predicting Temporary Deal Success. | |
| KR102078541B1 (en) | Issue interest based news value evaluation apparatus and method, storage media storing the same | |
| CN112200602A (en) | Neural network model training method and device for advertisement recommendation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |