CN110413759A - A kind of multi-platform user interaction data analysis method and system for from media - Google Patents
A kind of multi-platform user interaction data analysis method and system for from media Download PDFInfo
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Abstract
The present invention discloses a kind of multi-platform user interaction data analysis method for from media, the following steps are included: obtaining original contribution data and in the platform data of respective media platform publication, the platform data includes at least one set of mutual corresponding contribution data and user interaction data；Contribution data is matched with original contribution data, the original contribution data of successful match, contribution data and user interaction data corresponding with contribution data are pre-processed, it is associated with original contribution data to make the contribution data of successful match, and saves pretreated data；Analysis request is obtained, analysis is associated to pretreated original contribution data, contribution data and user interaction data according to analysis request, obtains corresponding analysis result.The present invention can carry out united analysis to the same user interaction data for issuing contribution on respective media platform from media number.
Description
Technical field
The present invention relates to propagate analysis field more particularly to a kind of multi-platform user interaction data analysis for from media
Method and system.
Background technique
Due to numerous from media platform now, in the operation from media, often same article is passed through different from matchmaker
Body platform is issued, and in the prior art, respective media platform can only be for the contribution that user is issued on the platform
User interaction data is for statistical analysis, therefore the technology that the prior art analyzes user interaction data has the disadvantage in that
1, same contribution can not bind directly respective media platform user interaction data it is for statistical analysis；
2, same user can not directly to respective media platform, it be compared and analyzed from the traffic-operating period of media number；
3, analysis dimension is single, can not carry out comprehensive analysis for channel, column belonging to contribution；
4, search condition is single, only supports keyword retrieval；
5, can not propagation condition to contribution it is for statistical analysis.
That is, can not be analyzed now in conjunction with multiple from user interaction data of the media platform to issued contribution, often
User oneself is needed to issue for statistical analysis could obtain of user interaction data for issuing contribution on each pair of media platform
The communication effect of contribution, intricate operation and low efficiency, therefore need to be further improved the prior art.
Summary of the invention
The shortcomings that present invention is directed in the prior art provides a kind of multi-platform user interaction data point for from media
Analyse method and system.
In order to solve the above-mentioned technical problem, the present invention is addressed by following technical proposals:
A kind of multi-platform user interaction data analysis method for from media, comprising the following steps:
It obtains original contribution data and in the platform data of respective media platform publication, the platform data includes at least
One group of mutual corresponding contribution data and user interaction data；
Contribution data is matched with original contribution data, by the original contribution data of successful match, contribution data, with
And user interaction data corresponding with contribution data is pre-processed, and the contribution data and original contribution data of successful match are made
It is associated, and save pretreated data；
Analysis request is obtained, according to analysis request to pretreated original contribution data, contribution data and user interaction
Data are associated analysis, obtain corresponding analysis result.
As an embodiment:
The original contribution data includes at least title, article ID and original article data, and the contribution data is at least wrapped
Include title；
Contribution data and original contribution data carry out matched specific steps are as follows:
The title of contribution data and the title of original contribution data are subjected to title match, when title match success, sentenced
It is set to successful match；
Otherwise, the article ID and/or article data for obtaining the contribution of title match failure, by the article ID and/or text
Chapter data are stored under corresponding contribution data, and carry out phase with original contribution data according to the article ID and/or article data
It is matched like degree, when similarity mode success, is determined as successful match.
As an embodiment, the article ID and/or article data of the contribution for obtaining title match failure, will
The article ID and/or article data are stored under corresponding contribution data, and according to the article ID and/or article data and original
The specific steps of beginning contribution data progress similarity mode are as follows:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution of several titles
Data set；
Obtain title match failure contribution article ID, judge in each contribution data of contribution data collection with the presence or absence of with
The corresponding article ID of original contribution data determines each contribution data of the contribution data collection when there are the article ID
With corresponding original contribution data successful match；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgement
When threshold value, each contribution data of corresponding contribution data collection and corresponding original contribution data successful match are determined.
As an embodiment, by the original contribution data of successful match, contribution data and with contribution data phase
Corresponding user interaction data is pre-processed, and it is associated with original contribution data to make the contribution data of successful match, and save
The specific steps of pretreated data are as follows:
The original contribution data includes at least article ID；
By the original contribution data of successful match, contribution data carry out labeling processing, wherein to original contribution data into
Contribution attribute tags are generated after row labelization processing, generate contribution publication label after carrying out labeling processing to contribution data, most
It will be saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing afterwards,
The contribution attribute tags include the article ID of matched original contribution data.
It as an embodiment, further include to the user interaction data saved after saving pretreated data
The step of being updated, specific steps are as follows:
Obtain user interaction data to be updated and corresponding contribution data, by the contribution data with saved
Contribution data is matched, and is carried out user interaction data to be updated after structuring processing to having saved according to matching result
User interaction data is updated.
As an embodiment, obtain analysis request, according to analysis request to pretreated original contribution data,
Contribution data and user interaction data are associated analysis, obtain the specific steps of corresponding analysis result are as follows:
Analysis request is obtained, according to analysis request to extracting user interaction data corresponding to related contribution data, and it is right
Gained user interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes opposite with analysis request
The contribution data answered, or the associated all contribution datas of original contribution data corresponding with analysis request.
The present invention also proposes a kind of multi-platform user interaction data analysis system for from media, comprising:
Data acquisition module, for obtaining original contribution data and in the platform data of respective media platform publication, institute
Stating platform data includes at least one set of mutual corresponding contribution data and user interaction data；
Relating module, for matching contribution data with original contribution data, by the original contribution number of successful match
It is pre-processed according to, contribution data and user interaction data corresponding with contribution data, makes the contribution data of successful match
It is associated with original contribution data, and save pretreated data；
Analysis module, for obtaining analysis request, according to analysis request to pretreated original contribution data, contribution number
It is associated analysis according to user interaction data, obtains corresponding analysis result.
As an embodiment:
The original contribution data includes at least title, article ID and original article data, and the contribution data is at least wrapped
Include title；
The relating module includes matching unit and pretreatment unit, the matching unit include title match subelement and
Similarity mode subelement；
The title match subelement, for the title of the title of contribution data and original contribution data to be carried out title
Match, when title match success, is determined as successful match；
The similarity mode subelement, the article ID and/or article data of the contribution for obtaining title match failure,
The article ID and/or article data are stored under corresponding contribution data, and according to the article ID and/or article data with
Original contribution data carries out similarity mode, when similarity mode success, is determined as successful match；
The similarity mode subelement is configured as:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution of several titles
Data set；
Obtain title match failure contribution article ID, judge in each contribution data of contribution data collection with the presence or absence of with
The corresponding article ID of original contribution data determines each contribution data of the contribution data collection when there are the article ID
With corresponding original contribution data successful match；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgement
When threshold value, each contribution data of corresponding contribution data collection and corresponding original contribution data successful match are determined.
As an embodiment, pretreatment unit is configured as:
The original contribution data includes at least article ID；
By the original contribution data of successful match, contribution data carry out labeling processing, wherein to original contribution data into
Contribution attribute tags are generated after row labelization processing, generate contribution publication label after carrying out labeling processing to contribution data, most
It will be saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing afterwards,
The contribution attribute tags include the article ID of matched original contribution data.
As an embodiment, the analysis module is configured as:
Analysis request is obtained, according to analysis request to extracting user interaction data corresponding to related contribution data, and it is right
Gained user interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes opposite with analysis request
The contribution data answered, or the associated all contribution datas of original contribution data corresponding with analysis request.
The present invention is due to using above technical scheme, with significant technical effect:
1, the contribution data by issuing respective media platform and original interactive data phase corresponding thereto of the invention
Association is analyzed in conjunction with multiple from the user interaction data of media platform to realize the analysis request inputted according to user.
2, design of the present invention to similarity mode step effectively avoids causing corresponding contribution data can not by change title
By title match situation associated with accordingly original contribution data, it can guarantee the integrality of data；
3, the present invention designs contribution data collection, and same contribution is converged in the contribution data that respective media platform obtains
Collection, contribution data concentrates any contribution data to match with original contribution data at this time, and contribution data can be made to concentrate all original texts
Number of packages can accelerate matching efficiency according to wanting to be associated with corresponding original contribution data；
4, the present invention carries out labeling processing to original contribution data, contribution data, allows users to through multiple dimensions
Retrieval analysis is carried out, a variety of needs of user to user interactive data analysis are met；
5, user interaction data of the present invention updates the design of step, can be avoided contribution data and original contribution data repeats
Matching improves matching efficiency.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other drawings based on these drawings.
Fig. 1 is a kind of flow diagram for the multi-platform user interaction data analysis method from media of the present invention；
Fig. 2 is platform data list schematic diagram in the step 301 of embodiment 1；
Fig. 3 is that platform data compares schematic diagram in the step 301 of embodiment 1；
Fig. 4 is umber one schematic diagram data in the step 301 of embodiment 1；
Fig. 5 is the schematic diagram of platform data comparison diagram in the step 301 of embodiment 1；
Fig. 6 be in the step 301 of embodiment 1 platform day quantized data analysis chart schematic diagram；
Fig. 7 is the schematic diagram that article issues seven day data tendency charts in the step 303 of embodiment 1；
Fig. 8 is a kind of module connection signal for the multi-platform user interaction data analysis system from media of the present invention
Figure；
Fig. 9 is the module connection diagram of relating module 200 in Fig. 8.
In figure, 100 it is data acquisition module, 200 is relating module, 300 is analysis module, 400 is update module, 210
It is matching unit, 220 be pretreatment unit, 211 be title match subelement, 212 is similarity mode subelement.
Specific embodiment
The present invention will be further described in detail below with reference to the embodiments, following embodiment be explanation of the invention and
The invention is not limited to following embodiments.
Embodiment 1, a kind of multi-platform user interaction data analysis method for from media, as shown in Figure 1, including following
Step:
S100, original contribution data is obtained and in the platform data of respective media platform publication, the platform data packet
Include at least one set of mutual corresponding contribution data and user interaction data；
S200, contribution data is matched with original contribution data, by the original contribution data of successful match, contribution number
Pre-processed according to and with the corresponding user interaction data of contribution data, make successful match contribution data and original original text
Number of packages evidence is associated, and saves pretreated data；
S300, analysis request is obtained, according to analysis request to pretreated original contribution data, contribution data and user
Interactive data is associated analysis, obtains corresponding analysis result.
It is generallyd use after editing platform edits contribution from media subscriber now, utilizes RSS (Really Simple
Syndication, Simple Syndication) be synchronized with it is multiple issued from media platform, therefore in the present embodiment obtain RSS link
(user's offer), obtains original contribution data by crawler.
Note: only needing to obtain original contribution data, without limiting acquisition modes, as user can according to the actual situation voluntarily
Import original contribution data.
Cookie is automatically generated from media platform login account by acquisition user's offer in the present embodiment, to guarantee
Can be corresponding from media platform with automated log on, acquire the platform data of corresponding the counted generation of platform；
The above-mentioned original contribution data of acquisition and the method for obtaining platform data belong to the prior art, therefore no longer in this specification
It is described in detail, the staff of related fields is also able to achieve.
To sum up, the present invention makes every part by the way that the contribution data issued in original contribution data and each platform to be associated
The contribution data issued in original contribution data and respective media platform is corresponding, and the contribution data of each platform and its user
Interactive data is corresponding, to realize the user interaction data progress statistical for the same contribution for issuing difference from media platform
Analysis.
In the step S200, contribution data and original contribution data carry out to matched specific step is as follows:
The original contribution data includes at least title, article ID and original article data, and the contribution data is at least wrapped
Include title；
Note: in the present embodiment, article ID can be obtained directly from RSS link；When being imported such as original contribution data by user,
Article ID can be exclusive identification code for user's setting or random generation, article ID.
The title of contribution data and the title of original contribution data are subjected to title match, when title match success, sentenced
It is set to successful match；
Otherwise, the article ID (article ID can be sky) and/or article data for obtaining the contribution of title match failure, will be described
Article ID and/or article data are stored under corresponding contribution data, and according to the article ID and/or article data and original original text
Number of packages is according to progress similarity mode；
When similarity mode success, it is determined as successful match.Since there are users according to actual needs to contribution title
The case where being replaced, therefore, same contribution may be different from the title on media platform in difference, therefore, exist can not with it is original
The contribution data that contribution data matches.
Contribution title carries out replacement and is generally divided into following two situation:
1., the title of original contribution data is replaced, after the entitled replacement of the original contribution data obtained at this time
Title, and be synchronized to respective media platform contribution will not synchronous vacations title, therefore the title of contribution data remains
Title before replacement, title of contribution data of respective media platform is identical in the case of this.
2., the title of contribution data is replaced, the only title after the entitled replacement of the contribution data, original at this time
Contribution data and other remained unchanged from the title of the contribution data of media platform.
Therefore this implementation is directed to the step of above situation increases similarity mode, to avoid since title is by modification/replacement
Lead to the case where it fails to match, can guarantee the integrality of data.
Further, the article ID and/or article data of the contribution for obtaining title match failure, by the article
ID and/or article data are stored under corresponding contribution data, and according to the article ID and/or article data and original contribution number
According to the specific steps for carrying out similarity mode are as follows:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution of several titles
Data set；When the title in original contribution data is modified, the title for the contribution data that respective media platform is issued is consistent
And it fails to match, therefore it includes an at least contribution data that contribution data, which is concentrated,.
The article ID for obtaining the contribution of title match failure, is stored in corresponding contribution data for acquired article ID at this time
In, and article ID can be sky.
Note: the article ID can be obtained from part from media platform, that is, from part contribution acquired in the media platform
Data ID containing article, therefore, original contribution data associated there can be directly judged according to article ID at this time.
Judge in each contribution data of contribution data collection with the presence or absence of article ID corresponding with original contribution data, when depositing
In the article ID, each contribution data of the contribution data collection and corresponding original contribution data successful match are determined；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgement
When threshold value, each contribution data of corresponding contribution data collection and corresponding original contribution data successful match are determined.
Since each contribution data of same contribution data collection belongs to same piece contribution, that is, itself and same original article data
Association, so when judge any contribution data and original article data successful match, that is, can determine that entire contribution data collection
Contribution data can accelerate matching efficiency to reduce calculation amount with corresponding original article data successful match.
Note: the step of above-mentioned title match, after first each contribution data can also be classified according to title, then title is carried out
Matching, and carry out title match and to each contribution data according to title classification when, can also according to actual needs combine publication when
Between, it avoids analyzing mistake caused by the different contributions of same title.
Use existing cosine-algorithm as the calculation of the algorithm and above-mentioned similarity calculation of title match in the present embodiment
Method carries out similarity calculation to article data and the original article data in the present embodiment method particularly includes:
Original article data and article data are reduced to two using keyword weight as the N-dimensional vector of component；Based on to
Model is measured, using two vectorial angle cosine values in vector space as the similarity degree for measuring two articles, cosine value is 0
Between~1, cosine value two articles of bigger explanation are more similar；
Finally whether article data and the original article data described in the similarity degree according to obtained by calculating are identical, this implementation
Judgment threshold is preset in example, when similarity degree is higher than judgment threshold, then determines that two documents are identical, otherwise determine two
For different documents.
Those skilled in the relevant art can sets itself judgment threshold according to actual needs, judgment threshold is set in the present embodiment
It is 95%, is i.e. when cosine value * 100% is higher than 95%, determines that two documents are identical.
Further, by the original contribution data of successful match, contribution data and and contribution data in step S200
Corresponding user interaction data is pre-processed, and it is associated with original contribution data to make the contribution data of successful match, and protect
Deposit the specific steps of pretreated data are as follows:
By the original contribution data of successful match, contribution data carry out labeling processing, wherein to original contribution data into
Contribution attribute tags are generated after row labelization processing, generate contribution publication label after carrying out labeling processing to contribution data, most
It will be saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing afterwards,
The contribution attribute tags include the article ID of matched original contribution data.
Due to ID containing article in the original contribution data, therefore the contribution data to match and initial data can be made to pass through text
Chapter ID is associated, that is, all contribution datas associated there can be obtained by the article ID in original contribution data, to unite
Meter analyzes corresponding with contribution data user interaction data, can be realized to same contribution in difference from media platform
User interaction data carries out comprehensive analysis.
In the present embodiment, contribution attribute tags include but is not limited to title, author, abstract, channel, column, issuing time；
It includes but is not limited to title, article ID, issuing time, platform, channel and column that contribution, which issues attribute,.
User interaction data includes but is not limited to amount of reading, the amount of thumbing up, reprinting amount and comment amount.
The present invention to original contribution data, contribution data carry out labeling processing, allow users to by multiple dimensions into
Row retrieval analysis meets a variety of needs of user to user interactive data analysis.
It further, further include being updated to the user interaction data saved after saving pretreated data
The step of, specific steps are as follows:
Obtain user interaction data to be updated and corresponding contribution data, by the contribution data with saved
Contribution data is matched, and is carried out user interaction data to be updated after structuring processing to having saved according to matching result
User interaction data is updated.
It follows that the above-mentioned article ID that adds in each contribution data can not only make contribution data and corresponding original contribution
Data match, moreover it is possible to convenient for the subsequent update to user interaction data, avoid repeating to match, improve and update efficiency.
After user obtains platform data, judge whether each user interaction data is user to be updated in the platform data
Interactive data, that is, contribution data each in platform data is matched with the contribution data saved, when successful match, nothing
The contribution data of successful match need to be matched again with original contribution data, be associated with, labeling processing and structuring processing, only
Need directly to be that user interaction data to be updated carries out for user interaction data corresponding to contribution data right after structuring processing
The user interaction data saved is updated.
It can be matched according to the title in contribution data with the title of the contribution data saved in the present embodiment, may be used also
The issuing time of contribution data and the contribution data saved is combined to realize matching according to actual needs.
Further, analysis request is obtained in step S300, according to analysis request to pretreated original contribution number
It is associated analysis according to, contribution data and user interaction data, obtains the specific steps of corresponding analysis result are as follows:
Analysis request is obtained, according to analysis request to extracting user interaction data corresponding to related contribution data, and it is right
Gained user interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes opposite with analysis request
The contribution data answered, or the associated all contribution datas of original contribution data corresponding with analysis request.
Above-mentioned analysis request is according to keyword, author, date, platform, channel, column, data volume or its any combination
The analysis request for multiple platform/mono- platforms that is proposed of mode；
301, analysis request is platform comparative analysis request:
Distinguished according to user interaction data of the platform comparative analysis of the acquisition request to media platform respective in the scheduled date
It is for statistical analysis.
The contribution data of issue date within the time period is extracted as related contribution data according to the scheduled date at this time, and
User interaction data corresponding to each related contribution data is counted according to the platform in contribution publication label, can be obtained
The user interaction situation of respective media platform in scheduled date, user interaction situation can be by platform data lists (such as Fig. 2 institute
Show), platform data ratio (as shown in Figure 3), umber one data (as shown in Figure 4), platform data comparison diagram (as shown in Figure 5), platform
The modes such as day quantized data analysis chart (as shown in Figure 6) show user.
Platform data list shows all data summation that article is issued within the respective media platform scheduled date, this implementation
Default carries out descending arrangement according to amount of reading in example.
Data accounting situation of the platform data than showing respective media platform, can click the amount of reading of top, user interaction,
The analysis type of the further user interaction data of reprinting amount is screened, and also may be selected to realize from media platform title in this implementation
Further to the screening of platform.
List data show under different data dimension, rank first platform.
Platform data comparison diagram shows the data volume pair of platform issued article each as unit of the day scheduled date Nei
Compare situation；
The content that platform day quantized data analysis chart is shown is that all publications are read in the article of each platform in the scheduled date
Amount, user interaction, reprinting amount odd-numbered day increasing value.
Gained user interaction situation can be further analyzed according to actual needs, as obtained respective media in the scheduled date
After the user interaction situation of platform, it can also propose that channel, column, contribution (title), author etc. retrieve the analysis request of element:
The user interaction data analysis that each platform is such as carried out for a certain channel, mentions from above-mentioned related contribution data at this time
Taking contribution attribute tags/contribution publication label be the contribution data of correspondence channel, to relative users interactive data according to platform into
Row statistical analysis.
The user interaction data analysis that each channel is such as carried out for a certain platform, mentions from above-mentioned related contribution data at this time
Taking contribution publication label is the contribution data of corresponding platform, for statistical analysis according to channel to relative users interactive data.
302, analysis request is channel/column comparative analysis request:
According to the channel of acquisition/column comparative analysis request to the user interaction data of channel/column each in the scheduled date
It is for statistical analysis respectively.
The contribution data of issue date within the time period is extracted as related contribution data according to the scheduled date at this time, and
User interaction data corresponding to each related contribution data is counted according to channel/column label, can be obtained specified
The user interaction situation of each channel/column in date, user interaction situation can pass through platform data list, platform data ratio, list
(principle shows user with step 301) to first data.
Note: when analysis request is that column data comparative analysis is requested in the present embodiment, needing specified channel, i.e., extraction channel and
The corresponding contribution data of column is as related contribution data；
303, analysis request is article comparative analysis request:
Statistical is carried out according to user interaction data of the article comparative analysis of the acquisition request to article each in the scheduled date
Analysis.
The related contribution data of issue date within the time period is being extracted according to the scheduled date, to each related contribution data
Corresponding user interaction data is counted, and can be obtained specified article user interaction situation within the scheduled date, and user is mutual
Emotionally condition can be by the way that article list, article issue seven day data tendency charts (as shown in Figure 7), umber one data show user.
The generation method that above-mentioned article issues seven day data tendency charts is as follows:
The user interaction data for obtaining each time point according to the preset period is for statistical analysis.
In the present embodiment, for article data, the frequency of a data is acquired by half an hour, each article is carried out
For the tracking of Shi Qitian, the corresponding user interaction data growth trend figure of each article is generated, embodies 7 days users after article publication
The growth trend of interactive data is realized for statistical analysis to the propagation condition of contribution.
To sum up, the analysis request that the present invention can be inputted according to user, user interaction data corresponding to related contribution data
It is for statistical analysis, it needs to show user interaction situation according to user, the list or accounting of customer analysis requirement is met as generated
Figure, list such as includes platform list, channel list, column list, article list, and above-mentioned list can pass through user interaction data
Carry out ascending or descending order sequence；Accounting figure such as needs to account for statistical result according to different platform, channel, column according to user
Than analyzing figure generated；The present invention can also periodically compare and analyze (as daily data between multi-platform, channel, column
The primary data interior for 24 hours of analysis)；
It further, further include data export step after saving pretreated data or obtaining corresponding analysis result
Suddenly, according to figure step are as follows:
Export request is obtained, the pretreated data or output analysis result saved according to export request output are corresponding
Analysis data.Convenient for work such as the subsequent performance appraisal of user, calculating, facilitate user according to actual needs flexibly with above-mentioned number
According to.
Embodiment 2, a kind of multi-platform user interaction data analysis system for from media, as shown in figure 8, including data
Obtain module 100, relating module 200, analysis module 300 and update module 400:
Data acquisition module 100, for obtain original contribution data and respective media platform publication platform data,
The platform data includes at least one set of mutual corresponding contribution data and user interaction data；
Relating module 200, for matching contribution data with original contribution data, by the original contribution of successful match
Data, contribution data and user interaction data corresponding with contribution data are pre-processed, and the contribution number of successful match is made
According to associated with original contribution data, and save pretreated data；
Analysis module 300, for obtaining analysis request, according to analysis request to pretreated original contribution data, original text
Number of packages evidence and user interaction data are associated analysis, obtain corresponding analysis result.
Update module 400, for obtaining user interaction data to be updated and corresponding contribution data, by the original text
User interaction data to be updated is carried out structuring according to being matched with the contribution data saved, according to matching result by number of packages
The user interaction data saved is updated after processing.
As shown in figure 9, the relating module 200 includes matching unit 210 and pretreatment unit 220, the matching unit
210 include title match subelement 211 and similarity mode subelement 212；
The title match subelement 211, for marking the title of contribution data and the title of original contribution data
Topic matching is determined as successful match when title match success；
Note: the original contribution data includes at least title, article ID and original article data, and the contribution data is at least
Including title；
The similarity mode subelement 212, the article ID and/or article number of the contribution for obtaining title match failure
According to the article ID and/or article data being stored under corresponding contribution data, and according to the article ID and/or article number
Similarity mode is carried out according to original contribution data, when similarity mode success, is determined as successful match；
Further, the similarity mode subelement 212 is configured as:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution of several titles
Data set；
Obtain title match failure contribution article ID, judge in each contribution data of contribution data collection with the presence or absence of with
The corresponding article ID of original contribution data determines each contribution data of the contribution data collection when there are the article ID
With corresponding original contribution data successful match；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgement
When threshold value, each contribution data of corresponding contribution data collection and corresponding original contribution data successful match are determined.
Pretreatment unit 220 is configured as:
The original contribution data includes at least article ID；
By the original contribution data of successful match, contribution data carry out labeling processing, wherein to original contribution data into
Contribution attribute tags are generated after row labelization processing, generate contribution publication label after carrying out labeling processing to contribution data, most
It will be saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing afterwards,
The contribution attribute tags include the article ID of matched original contribution data.
Further, the analysis module 300 is configured as:
Analysis request is obtained, according to analysis request to extracting user interaction data corresponding to related contribution data, and it is right
Gained user interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes opposite with analysis request
The contribution data answered, or the associated all contribution datas of original contribution data corresponding with analysis request.
For device embodiment, since it is basically similar to the method embodiment, related so being described relatively simple
Place illustrates referring to the part of embodiment of the method.
All the embodiments in this specification are described in a progressive manner, the highlights of each of the examples are with
The difference of other embodiments, the same or similar parts between the embodiments can be referred to each other.
It should be understood by those skilled in the art that, the embodiment of the present invention can provide as method, apparatus or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the present invention, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The present invention be referring to according to the method for the present invention, the flow chart of terminal device (system) and computer program product
And/or block diagram describes.It should be understood that each process in flowchart and/or the block diagram can be realized by computer program instructions
And/or the combination of the process and/or box in box and flowchart and/or the block diagram.It can provide these computer programs to refer to
Enable the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing terminal devices with
A machine is generated, so that generating by the instruction that computer or the processor of other programmable data processing terminal devices execute
For realizing the function of being specified in one or more flows of the flowchart and/or one or more blocks of the block diagram
Device.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing terminal devices
In computer-readable memory operate in a specific manner, so that instruction stored in the computer readable memory generates packet
The manufacture of command device is included, which realizes in one side of one or more flows of the flowchart and/or block diagram
The function of being specified in frame or multiple boxes.
These computer program instructions can also be loaded into computer or other programmable data processing terminal devices, so that
Series of operation steps are executed on computer or other programmable terminal equipments to generate computer implemented processing, thus
The instruction executed on computer or other programmable terminal equipments is provided for realizing in one or more flows of the flowchart
And/or in one or more blocks of the block diagram specify function the step of.
It should be understood that
" one embodiment " or " embodiment " mentioned in specification means the special characteristic described in conjunction with the embodiments, structure
Or characteristic is included at least one embodiment of the present invention.Therefore, the phrase " reality that specification various places throughout occurs
Apply example " or " embodiment " the same embodiment might not be referred both to.
Although preferred embodiments of the present invention have been described, it is created once a person skilled in the art knows basic
Property concept, then additional changes and modifications can be made to these embodiments.So it includes excellent that the following claims are intended to be interpreted as
It selects embodiment and falls into all change and modification of the scope of the invention.
In addition, it should be noted that, the specific embodiments described in this specification, the shape of parts and components are named
Title etc. can be different.The equivalent or simple change that all structure, feature and principles described according to the invention patent design are done, is wrapped
It includes in the scope of protection of the patent of the present invention.Those skilled in the art can be to described specific implementation
Example is done various modifications or additions or is substituted in a similar manner, and without departing from structure of the invention or surmounts this
Range as defined in the claims, is within the scope of protection of the invention.
Claims (10)
1. a kind of multi-platform user interaction data analysis method for from media, which comprises the following steps:
It obtains original contribution data and in the platform data of respective media platform publication, the platform data includes at least one set
Mutual corresponding contribution data and user interaction data；
Contribution data is matched with original contribution data, by the original contribution data of successful match, contribution data, Yi Jiyu
The corresponding user interaction data of contribution data is pre-processed, and keeps the contribution data of successful match related to original contribution data
Connection, and save pretreated data；
Analysis request is obtained, according to analysis request to pretreated original contribution data, contribution data and user interaction data
It is associated analysis, obtains corresponding analysis result.
2. a kind of multi-platform user interaction data analysis method for from media according to claim 1, feature exist
In:
The original contribution data includes at least title, article ID and original article data, and the contribution data includes at least mark
Topic；
Contribution data and original contribution data carry out matched specific steps are as follows:
The title of contribution data and the title of original contribution data are subjected to title match, when title match success, are determined as
Successful match；
Otherwise, the article ID and/or article data for obtaining the contribution of title match failure, by the article ID and/or article number
Similarity is carried out according to being stored under corresponding contribution data, and according to the article ID and/or article data and original contribution data
Matching is determined as successful match when similarity mode success.
3. a kind of multi-platform user interaction data analysis method for from media according to claim 2, feature exist
In the article ID and/or article data of the contribution for obtaining title match failure, by the article ID and/or article data
It is stored under corresponding contribution data, and similarity is carried out according to the article ID and/or article data and original contribution data
The specific steps matched are as follows:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution data of several titles
Collection；
Obtain title match failure contribution article ID, judge in each contribution data of contribution data collection with the presence or absence of with it is original
The corresponding article ID of contribution data, when there are the article ID, determine each contribution data of the contribution data collection with it is right
Answer original contribution data successful match；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgment threshold
When, determine each contribution data of corresponding contribution data collection and corresponding original contribution data successful match.
4. any a kind of multi-platform user interaction data analysis method for from media according to claim 1~3,
It is characterized in that, by the original contribution data of successful match, contribution data and user interaction data corresponding with contribution data
It is pre-processed, it is associated with original contribution data to make the contribution data of successful match, and save the tool of pretreated data
Body step are as follows:
The original contribution data includes at least article ID；
The original contribution data of successful match, contribution data are subjected to labeling processing, wherein marking to original contribution data
Contribution attribute tags are generated after labelization processing, contribution publication label is generated after carrying out labeling processing to contribution data, finally will
It is saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing, it is described
Contribution attribute tags include the article ID of matched original contribution data.
5. a kind of multi-platform user interaction data analysis method for from media according to any one of claims 1 to 3,
It is characterized in that, after saving pretreated data, further includes the steps that being updated the user interaction data saved, specifically
Step are as follows:
User interaction data to be updated and corresponding contribution data are obtained, by the contribution data and the contribution saved
Data are matched, and are carried out user interaction data to be updated after structuring processing to the user saved according to matching result
Interactive data is updated.
6. a kind of multi-platform user interaction data analysis method for from media according to any one of claims 1 to 3,
It is characterized in that, analysis request is obtained, according to analysis request to pretreated original contribution data, contribution data and user interaction
Data are associated analysis, obtain the specific steps of corresponding analysis result are as follows:
Analysis request is obtained, according to analysis request to user interaction data corresponding to the related contribution data of extraction, and to gained
User interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes corresponding with analysis request
Contribution data, or the associated all contribution datas of original contribution data corresponding with analysis request.
7. a kind of multi-platform user interaction data analysis system for from media characterized by comprising
Data acquisition module is described flat for obtaining original contribution data and in the platform data of respective media platform publication
Number of units is according to including at least one set of mutual corresponding contribution data and user interaction data；
Relating module, for matching contribution data with original contribution data, by the original contribution data of successful match, original text
Number of packages evidence and user interaction data corresponding with contribution data are pre-processed, and the contribution data and original of successful match are made
Beginning contribution data is associated, and saves pretreated data；
Analysis module, for obtaining analysis request, according to analysis request to pretreated original contribution data, contribution data and
User interaction data is associated analysis, obtains corresponding analysis result.
8. a kind of multi-platform user interaction data analysis system for from media according to claim 7, feature exist
In:
The original contribution data includes at least title, article ID and original article data, and the contribution data includes at least mark
Topic；
The relating module includes matching unit and pretreatment unit, and the matching unit includes title match subelement and similar
Spend coupling subelement；
The title match subelement, for the title of the title of contribution data and original contribution data to be carried out title match,
When title match success, it is determined as successful match；
The similarity mode subelement, the article ID and/or article data of the contribution for obtaining title match failure, by institute
State article ID and/or article data be stored under corresponding contribution data, and according to the article ID and/or article data with it is original
Contribution data carries out similarity mode, when similarity mode success, is determined as successful match；
The similarity mode subelement is configured as:
Classified according to contribution data of the title to the failure of all title matches, obtains the identical contribution data of several titles
Collection；
Obtain title match failure contribution article ID, judge in each contribution data of contribution data collection with the presence or absence of with it is original
The corresponding article ID of contribution data, when there are the article ID, determine each contribution data of the contribution data collection with it is right
Answer original contribution data successful match；
When the article ID is not present, obtains contribution data and concentrate the corresponding article data of any contribution data；
The article data and each original article data are subjected to similarity calculation, when calculated result is greater than preset judgment threshold
When, determine each contribution data of corresponding contribution data collection and corresponding original contribution data successful match.
9. a kind of multi-platform user interaction data analysis system for from media according to claim 8, feature exist
In pretreatment unit is configured as:
The original contribution data includes at least article ID；
The original contribution data of successful match, contribution data are subjected to labeling processing, wherein marking to original contribution data
Contribution attribute tags are generated after labelization processing, contribution publication label is generated after carrying out labeling processing to contribution data, finally will
It is saved after original contribution data and respective labels, contribution data and respective labels and user interaction data structuring processing, it is described
Contribution attribute tags include the article ID of matched original contribution data.
10. according to a kind of any multi-platform user interaction data analysis system for from media of claim 7 to 9,
It is characterized in that, the analysis module is configured as:
Analysis request is obtained, according to analysis request to user interaction data corresponding to the related contribution data of extraction, and to gained
User interaction data is for statistical analysis, obtains analysis result；The correlation contribution data includes corresponding with analysis request
Contribution data, or the associated all contribution datas of original contribution data corresponding with analysis request.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111246226A (en) * | 2020-01-04 | 2020-06-05 | 厦门二五八网络科技集团股份有限公司 | Management method and system for new media platform |
| CN111913947A (en) * | 2020-09-21 | 2020-11-10 | 重庆佬壹家科技有限公司 | Data integration system of take-out platform |
| CN113127762A (en) * | 2021-04-21 | 2021-07-16 | 北京字节跳动网络技术有限公司 | Information display method, information processing method and device |
| CN113158064A (en) * | 2021-05-11 | 2021-07-23 | 两比特(北京)科技有限公司 | Cloud data short video data capturing and statistical summarization analysis algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102541912A (en) * | 2010-12-17 | 2012-07-04 | 北大方正集团有限公司 | System and method for evaluating propagating influences of online articles |
| US20160300287A1 (en) * | 2012-12-20 | 2016-10-13 | Ebay Inc. | View item related searches |
| CN107577688A (en) * | 2017-04-25 | 2018-01-12 | 上海市互联网信息办公室 | Original article influence power analysis system based on media information collection |
| CN109241297A (en) * | 2018-07-09 | 2019-01-18 | 广州品唯软件有限公司 | A kind of classifying content polymerization, electronic equipment, storage medium and engine |
-
2019
- 2019-07-31 CN CN201910702592.4A patent/CN110413759A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102541912A (en) * | 2010-12-17 | 2012-07-04 | 北大方正集团有限公司 | System and method for evaluating propagating influences of online articles |
| US20160300287A1 (en) * | 2012-12-20 | 2016-10-13 | Ebay Inc. | View item related searches |
| CN107577688A (en) * | 2017-04-25 | 2018-01-12 | 上海市互联网信息办公室 | Original article influence power analysis system based on media information collection |
| CN109241297A (en) * | 2018-07-09 | 2019-01-18 | 广州品唯软件有限公司 | A kind of classifying content polymerization, electronic equipment, storage medium and engine |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111246226A (en) * | 2020-01-04 | 2020-06-05 | 厦门二五八网络科技集团股份有限公司 | Management method and system for new media platform |
| CN111913947A (en) * | 2020-09-21 | 2020-11-10 | 重庆佬壹家科技有限公司 | Data integration system of take-out platform |
| CN113127762A (en) * | 2021-04-21 | 2021-07-16 | 北京字节跳动网络技术有限公司 | Information display method, information processing method and device |
| CN113158064A (en) * | 2021-05-11 | 2021-07-23 | 两比特(北京)科技有限公司 | Cloud data short video data capturing and statistical summarization analysis algorithm |
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