CN104978665A - Brand evaluation method and brand evaluation device - Google Patents
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Abstract
The embodiment of the invention provides a brand evaluation method and a brand evaluation device. The method comprises the steps: receiving a brand keyword and a brand attribute; determining a brand data source associated with the brand attribute in the Internet and acquiring brand data associated with the brand keyword from the brand data source; and exposing and analyzing the brand data associated with the brand keyword to determine an expression index of the brand keyword, carrying out text analysis on the brand data associated with the brand keyword to determine a value index of the brand keyword, carrying out forward movement analysis on the brand data associated with the brand keyword to determine a recommended index of the brand keyword, and calculating the weighted values of the expression index, the value index and the recommended index according to a predetermined weight.
Description
Technical field
Embodiment of the present invention relates to technical field of information processing, more specifically, relates to a kind of evaluation of brands method and apparatus.
Background technology
Along with the develop rapidly of computer technology and network technology, the effect that internet (Internet) plays in daily life, study and work is also increasing.And along with the development of mobile Internet, internet is also developing to mobile.
Society has entered the highly developed information age, its competition among enterprises form also by the past mainly based on the compound form of competition that the singular competition Morphological Transitions of product function quality is using corporate image, commodity, brand etc. as important means and main tendency.The transformation of this form and modern media are shown and mutual developing rapidly of cause is undivided.
Brand is that people are to the one evaluation of enterprise and products thereof, after sale service, cultureal value and cognition.Brand is the product or the service that identify certain sellers or certain crowd of sellers, and make it with the product of rival or serve the trade name and mark thereof distinguishing and come, be usually made up of the combination of the key elements such as word, mark, symbol, pattern and color or these key elements.
In the prior art, the method for evaluation of brands mainly comprises: cost measurement method, market price measurement Law and income measurement method.
But current evaluation of brands mode lays particular emphasis on price factor and weighs, and lack the comprehensive assessment to brand, therefore accuracy is not high.
Summary of the invention
Embodiment of the present invention proposes a kind of evaluation of brands method and apparatus, based on the comprehensive assessment to brand, thus improves assessment accuracy.
The concrete scheme of embodiment of the present invention is as follows:
Propose a kind of evaluation of brands method according to the one side of embodiment of the present invention, the method comprises:
Receive BRANDKEYS and brand generic;
Determine branding data source relevant to described brand generic in internet, and obtain the branding data relevant to described BRANDKEYS from described branding data source;
Carry out exposure to the described branding data relevant to BRANDKEYS to resolve with the performance indicators determining described BRANDKEYS, text resolution is carried out to determine the value index of described BRANDKEYS to the described branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the described branding data relevant to BRANDKEYS, to determine the fertilizer index of described BRANDKEYS, and based on the weighted value of performance indicators, value index and fertilizer index described in the weight calculation pre-set.
Preferably, describedly determine branding data source relevant to brand generic in internet, and obtain the branding data relevant to BRANDKEYS from described branding data source and comprise:
Determine vertical source of media relevant to described brand generic in internet;
Obtain with directed reptile mode or wide area reptile mode the media data comprising described BRANDKEYS from described vertical source of media.
Preferably, described to the branding data relevant to BRANDKEYS carry out exposure resolve with the performance indicators determining described BRANDKEYS, comprising:
Determine the sub-index of history exposure of described BRANDKEYS and the sub-index of cognition degree of described BRANDKEYS;
Based on the weighted value of the sub-index of history exposure and the sub-index of cognition degree described in the weight calculation pre-set, the weighted value of the sub-index of described history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
Preferably, text resolution is carried out to determine the value index of BRANDKEYS to the described branding data relevant to BRANDKEYS, comprising:
Semantics identity is carried out to the described branding data relevant to BRANDKEYS, and determines based on semantics identity result the sub-index of reputation described BRANDKEYS being presented to front assessment;
Pageview statistics is carried out to the described branding data relevant to BRANDKEYS, and determine to browse based on pageview statistics the number of users that branding data that described BRANDKEYS is correlated with exceedes predetermined threshold, and based on the sub-index of described number of users determination degree of recognition;
Based on the weighted value of the sub-index of reputation and the sub-index of degree of recognition described in the weight calculation pre-set, the weighted value of the sub-index of described reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
Preferably, describedly semantics identity carried out to the branding data relevant to BRANDKEYS comprise:
Utilize symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index;
Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
Preferably, describedly forwarding behavior parsing is carried out to the branding data relevant to BRANDKEYS, to determine that the fertilizer index of described BRANDKEYS comprises:
Determine the recommendation number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the recommendation of the branding data relevant to BRANDKEYS based on described recommendation number of users;
Determine the forwarding number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the forwarding of the branding data relevant to BRANDKEYS based on described forwarding number of users;
Based on the weighted value of recommending sub-index and the sub-index of forwarding described in the weight calculation pre-set, the sub-index of described recommendation and the fertilizer index of weighted value as this BRANDKEYS forwarding sub-index.
Preferably, described brand generic comprises the industry attributive classification information of this brand.
Preferably, described branding data source comprises: news address, speech address, community or focus top news address.
According to the another aspect of embodiment of the present invention, propose a kind of evaluation of brands device, this device comprises:
Receiver module, for receiving BRANDKEYS and brand generic;
Data acquisition module, for determining branding data source relevant to described brand generic in internet, and obtains the branding data relevant to described BRANDKEYS from described branding data source;
Computing module, resolve for carrying out exposure to the described branding data relevant to BRANDKEYS with the performance indicators determining described BRANDKEYS, text resolution is carried out to determine the value index of described BRANDKEYS to the described branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the described branding data relevant to BRANDKEYS, to determine the fertilizer index of described BRANDKEYS, and based on the weighted value of performance indicators, value index and fertilizer index described in the weight calculation pre-set.
Preferably, data acquisition module, for determining vertical source of media relevant to described brand generic in internet; Obtain with directed reptile mode or wide area reptile mode the media data comprising described BRANDKEYS from described vertical source of media.
Preferably, computing module, for the sub-index of cognition degree of the sub-index of history exposure and described BRANDKEYS of determining described BRANDKEYS; Based on the weighted value of the sub-index of history exposure and the sub-index of cognition degree described in the weight calculation pre-set, the weighted value of the sub-index of described history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
Preferably, computing module, for carrying out semantics identity to the described branding data relevant to BRANDKEYS, and determines based on semantics identity result the sub-index of reputation described BRANDKEYS being presented to front assessment; Pageview statistics is carried out to the described branding data relevant to BRANDKEYS, and determines to browse based on pageview statistics the number of users that branding data that described BRANDKEYS is correlated with exceedes predetermined threshold, than can based on the sub-index of number of users determination degree of recognition; Based on the weighted value of the sub-index of reputation and the sub-index of degree of recognition described in the weight calculation pre-set, the weighted value of the sub-index of described reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
Preferably, computing module, for utilizing symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index; Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
Preferably, computing module, for determining the recommendation number of users of described relevant to BRANDKEYS branding data, and determines the sub-index of the recommendation of the branding data relevant to BRANDKEYS based on described recommendation number of users; Determine the forwarding number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the forwarding of the branding data relevant to BRANDKEYS based on described forwarding number of users; Based on the weighted value of recommending sub-index and the sub-index of forwarding described in the weight calculation pre-set, the sub-index of described recommendation and the fertilizer index of weighted value as this BRANDKEYS forwarding sub-index.
Preferably, described brand generic comprises the industry attributive classification information of this brand.
Preferably, described branding data source comprises: news address, speech address, community or focus top news address.
As can be seen from technique scheme, in embodiments of the present invention, BRANDKEYS and brand generic is received; Determine branding data source relevant to brand generic in internet, and obtain the branding data relevant to BRANDKEYS from branding data source; Carry out exposure to the branding data relevant to BRANDKEYS to resolve with the performance indicators determining BRANDKEYS, text resolution is carried out to determine the value index of BRANDKEYS to the branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the branding data relevant to BRANDKEYS, to determine the fertilizer index of BRANDKEYS, and based on the weighted value of the weight calculation performance indicators, value index and the fertilizer index that pre-set.As can be seen here, embodiment of the present invention calculates evaluation of brands value based on performance indicators, value index and fertilizer index three dimensions, and be different from prior art and only weigh for price factor, therefore evaluation of brands value result of calculation is more accurate.
In addition, by the sub-index of exposure, the sub-index of cognition degree, the sub-index of reputation, the sub-index of degree of recognition, the assessed value of recommending sub-index, forwarding the many factors comprehensive assessment brands such as sub-index.Accurately can locate the problem place of abnormal brand.
Accompanying drawing explanation
Fig. 1 is the evaluation of brands method flow diagram according to embodiment of the present invention;
Fig. 2 is the model schematic of the evaluation of brands value according to embodiment of the present invention;
Fig. 3 is the evaluation of brands method exemplary flow chart according to embodiment of the present invention;
Fig. 4 is the evaluation of brands structure drawing of device according to embodiment of the present invention;
Fig. 5 is the internet system architectural configurations figure of the evaluation of brands of embodiment of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with accompanying drawing, the present invention is described in further detail.
In order to make technical scheme of the present invention and advantage clearly understand, below in conjunction with drawings and the embodiments, the present invention is further elaborated.Should be appreciated that embodiment described herein only in order to illustrative explanation the present invention, the protection domain be not intended to limit the present invention.
Succinct and directly perceived in order to what describe, hereafter by description some representational embodiments, the solution of the present invention is set forth.Details a large amount of in embodiment only understands the solution of the present invention for helping.But these details can be not limited to when clearly, technical scheme of the present invention realizes.In order to avoid unnecessarily fuzzy the solution of the present invention, some embodiments do not describe meticulously, but only give framework.Hereinafter, " comprising " refers to " including but not limited to ", " according to ... " refer to " at least basis ..., but be not limited to only basis ... ".Due to the speech habits of Chinese, when hereinafter not particularly pointing out the quantity of a composition, mean that this composition can be one also can be multiple, or can be regarded as at least one.
In embodiments of the present invention, creativeness provides the concept of evaluation of brands value.Evaluation of brands value, refers to the synthesized competitiveness of various brands on market, each field.Evaluation of brands value can provide long-term for the enterprise in every profession and trade, continuous print branding data index, can from transversely reflecting the status of this brand on each market, again can longitudinal direction the historical data of relatively this brand, depict the precise trajectory of brand synthesized competitiveness development, and predict the future trend of brand development.
In embodiments of the present invention, by obtaining the outside public sentiment data of the enterprise/product relevant to brand, its evaluation of brands value of comprehensive analysis and evaluation.
Fig. 1 is the evaluation of brands method flow diagram according to embodiment of the present invention.
As shown in Figure 1, the method comprises:
Step 101: receive BRANDKEYS and brand generic.
Here, brand generic comprises the industry attributive classification information of this brand.Such as, user inputs BRANDKEYS: " apple " and brand generic: " electronic product "; For another example, user inputs BRANDKEYS: " Sohu " and brand generic: " Internet enterprises ".
Step 102: determine branding data source relevant to brand generic in internet, and obtain the branding data relevant to described BRANDKEYS from branding data source.
Such as, branding data source comprises: news address, speech address, community or focus top news address, etc.
Here, first based on branding data source relevant to brand generic in brand generic determination internet.Such as, when brand generic is " electronic product ", branding data source can be the vertical media relevant to electronic product.Then, obtain with directed reptile mode or wide area reptile mode the media data comprising BRANDKEYS from vertical media.
Step 103: exposure is carried out to the branding data relevant to BRANDKEYS and resolves with the performance indicators determining BRANDKEYS, text resolution is carried out to determine the value index of BRANDKEYS to the branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the branding data relevant to BRANDKEYS, to determine the fertilizer index of BRANDKEYS, and based on the weighted value of the weight calculation performance indicators, value index and the fertilizer index that pre-set.
Fig. 2 is the model schematic of the evaluation of brands value according to embodiment of the present invention.
In embodiments of the present invention, evaluation of brands value is calculated based on performance indicators, value index and fertilizer index three dimensions.Performance indicators is for showing the performance dimension of brand; Value index is for showing brand's equity dimension; Fertilizer index is for showing the recommendation dimension of brand.
Specifically, performance indicators comprises history and exposes sub-index (how many people had once seen brand), the sub-index of cognition degree (how many people know brand); Value index comprises the sub-index of reputation (how many people like brand), the sub-index of degree of recognition (how many people often see brand), recommends sub-index (how many people promises to undertake also can see brand from now on), forward sub-index (how many people recommend him and see).
For the branding data relevant to BRANDKEYS.Can the specifically process such as execution contexts process, text analyzing, word frequency statistics, correlation analysis, to obtain performance indicators, value index and fertilizer index.
In one embodiment, based on the sub-index of history exposure of the once number determination BRANDKEYS of this brand browsed, and based on knowing the sub-index of cognition degree of number determination BRANDKEYS of this brand; Again based on the weighted value of the sub-index of weight calculation history exposure pre-set and the sub-index of cognition degree, wherein the weighted value of the sub-index of history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
In one embodiment, based on the sub-index of reputation of the number determination BRANDKEYS of this brand of appreciation, and based on frequently seeing the sub-index of number of users determination degree of recognition of this brand; Again based on the weighted value of the sub-index of weight calculation reputation pre-set and the sub-index of degree of recognition, wherein the weighted value of the sub-index of reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
In one embodiment, based on the sub-index of recommendation of number determination BRANDKEYS also seeing brand from now on, and see that the number of users of this brand is determined to forward sub-index based on recommended; Recommend sub-index based on the weight calculation pre-set again and forward the weighted value of sub-index, wherein recommending the performance indicators of weighted value as this BRANDKEYS of sub-index and the sub-index of forwarding.
In one embodiment, carry out semantics identity to the branding data relevant to BRANDKEYS to comprise:
Utilize symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index;
Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
Such as, a statement to be analyzed comprises linear structure and keyword (i.e. language block).Wherein, the key of semantics identity is the linear structure identifying statement.Meaning of language is hidden in the middle of the linear structure of statement, and the linear structure of statement is equivalent to the constant of language.The meaning of one's words and even meaning and thinking are all hidden in the linear structure of statement, by the linear structure of anolytic sentence, can reach the object identifying intention.Keyword is equivalent to the variable of language.By replacing appropriate section (i.e. variable), its meaning of one's words substantially all can retain, and can obtain retrieving comparatively accurately or translation result.
Some conventional inherent nouns and/or gerund can be defined as constant, but variable is not limited to inherent noun and/or gerund.In some cases, variable also can be a kind of conventional phrase, even long sentence.In addition, when determining constant and linear structure, dividing mode may not be unique.For the dividing mode that variable is minimum, the linear structure corresponding to it is called minimal linear structure.Usually, variable is fewer, can think that the corresponding information expressed by linear structure is more abundant, then the information of corresponding search is more accurate.
Based on above-mentioned analysis, by carrying out above-mentioned cutting to lot of documents (comprising web documents, blog, textbook, various electronic documents etc.), we just can obtain sufficient linear structure storehouse and keyword (i.e. language block) storehouse, are original sampling data.
More than describe a kind of text analyzing mode in detail, it will be appreciated by those of skill in the art that this description is only exemplary, the protection domain be not intended to limit the present invention.
Fig. 3 is the evaluation of brands method exemplary flow chart according to embodiment of the present invention.
As shown in Figure 3, the method comprises:
Step 301: the BRANDKEYS and the brand generic that receive user's input.
Step 302: judge whether this locality exists this BRANDKEYS and assessed value thereof, if there is no performs step 303 and subsequent step thereof; Step 306 is performed if existed.
Step 303: based on storehouse, brand generic determination branding data source, and crawl the branding data relevant to BRANDKEYS from this storehouse, branding data source orientation.The every data address of brand health degree model is store, such as: news address, speech address, community, focus top news address in storehouse, branding data source.
Step 304: for the branding data obtained, carries out the warehouse-in on basis, cleaning and data normalization process.
Step 305: concrete analysis work branding data being comprised to the analysis of public opinion, comprising text-processing, text analyzing, word frequency statistics, correlation analysis, to obtain performance indicators, value index and fertilizer index respectively, and calculate the weighted sum of performance indicators, value index and fertilizer index, as evaluation of brands value, and show brand assessed value, exit this flow process.
Based on flow process shown in Fig. 3, a representative instance of application embodiment of the present invention is described below.
Such as, for the brand of certain company's game products, carry out analysis and assessment:
First, obtain the Domestic News amount of game products, Baidu's index, each World Jam information content and user comment situation, forum user information, micro-blog information issue amount and inversion quantity by reptile orientation, and calculate following data target respectively:
Exposure index: article information page arrival amount is such as 2000 information;
Cognition degree: such as with Baidu's index for cognition degree index, be exemplified as 2360;
Reputation index: forum information is excellent comments accounting (just negative belonging to text emotion analysis every bar information), is such as 34%;
Degree of recognition index: calculating degree of recognition index based on forum user week login frequency (logging in frequency according to forum user in one week), is such as 5634;
Loyalty index: calculating loyalty index based on forum user Zhou Denglu accounting (last week, login user logged in accounting this week), is such as 40%;
Recommendation degree index: based on microblogging associated topic secondary reprinting amount accounting (accounting that in Sina's microblogging, product information is converted after being converted once again) calculated recommendation degree index, be such as 10%.
Then, to above 6 item numbers according to carrying out standardization (utilizing multiplicity sampling data), show that the assessed value (1 ~ 10 is interval) of brand is 8 points.
Technical scheme proposed by the invention specifically can be implemented in evaluation of brands platform.Below again in conjunction with a concrete example, be described in more detail exemplary embodiment of the present invention.
The first step: user is at evaluation of brands platform inputted search key word: " the semi-gods and the semi-devils 3D "; Wherein " the semi-gods and the semi-devils 3D " is BRANDKEYS of the present invention.
Second step: evaluation of brands platform crawls branding data relevant to " the semi-gods and the semi-devils 3D " within the schedule time (such as one month) by directed reptile, data type specifically can comprise: Baidu's index, Google trend, Domestic News, ad data, channel data, microblogging temperature, forum's public feelings information, etc.
3rd step: evaluation of brands platform carries out the process such as data cleansing, standardization, text analyzing, analytical model coupling according to curing model to the affiliated brand data obtained.
4th step: evaluation of brands platform carries out Data Matching calculating according to brand health degree model, calculates the sub-indexs such as history exposure, cognition degree, reputation, degree of recognition, loyalty and recommendation degree respectively, wherein:
History exposure=standardized advertisement number+standardization Domestic News number+standardization channel data;
Cognition degree=standardized advertisement clicking rate+standardization news response rate+standardization channel position data click rate;
The excellent comparation and assessment of reputation=standardization forums information+excellent comparation and assessment of standardization Domestic News;
Degree of recognition=standardization (forum's public feelings information discussion amount/forum's membership)+standardization (microblogging is paid close attention to discussion amount/microblogging and paid close attention to number)+standardization (information reply volume/information page PV visit capacity);
Loyalty=standardization (microblogging pays close attention to reply volume/microblogging bean vermicelli amount)+standardization (product forum member reply volume/product forum member amount);
Recommendation degree=standardization (microblogging pays close attention to member's transfer amount/microblogging bean vermicelli amount);
5th step: go out history exposure, cognition degree, reputation, degree of recognition, loyalty and recommendation degree based on four-step calculation, weighted calculation goes out final brand health degree, wherein:
Brand health degree=5* exposure+4* degree of recognition+5* reputation+3* degree of recognition+2* loyalty+1* recommendation degree;
Brand health degree is evaluation of brands value.Such as, calculate " the semi-gods and the semi-devils 3D " in May, 2015 overall brand health degree be 89 points (centesimal system).
More than describe the example procedure of the brand health degree of calculating " the semi-gods and the semi-devils 3D " in detail.It will be appreciated by those of skill in the art that this description is only exemplary, be not intended to limit the present invention the protection domain of embodiment.
Based on above-mentioned analysis, the invention allows for a kind of evaluation of brands device.
Fig. 4 is the evaluation of brands structure drawing of device according to embodiment of the present invention.
As shown in Figure 4, this device 400 comprises:
Receiver module 401, for receiving BRANDKEYS and brand generic;
Data acquisition module 402, for determining branding data source relevant to described brand generic in internet, and obtains the branding data relevant to described BRANDKEYS from described branding data source;
Computing module 403, resolve for carrying out exposure to the described branding data relevant to BRANDKEYS with the performance indicators determining described BRANDKEYS, text resolution is carried out to determine the value index of described BRANDKEYS to the described branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the described branding data relevant to BRANDKEYS, to determine the fertilizer index of described BRANDKEYS, and based on the weighted value of performance indicators, value index and fertilizer index described in the weight calculation pre-set.
In one embodiment, data acquisition module 401, for determining vertical source of media relevant to described brand generic in internet; Obtain with directed reptile mode or wide area reptile mode the media data comprising described BRANDKEYS from described vertical source of media.
In one embodiment, computing module 402, for the sub-index of cognition degree of the sub-index of history exposure and described BRANDKEYS of determining described BRANDKEYS; Based on the weighted value of the sub-index of history exposure and the sub-index of cognition degree described in the weight calculation pre-set, the weighted value of the sub-index of described history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
In one embodiment, computing module 402, for carrying out semantics identity to the described branding data relevant to BRANDKEYS, and determines based on semantics identity result the sub-index of reputation described BRANDKEYS being presented to front assessment; Pageview statistics is carried out to the described branding data relevant to BRANDKEYS, and determines to browse based on pageview statistics the number of users that branding data that described BRANDKEYS is correlated with exceedes predetermined threshold, than can based on the sub-index of number of users determination degree of recognition; Based on the weighted value of the sub-index of reputation and the sub-index of degree of recognition described in the weight calculation pre-set, the weighted value of the sub-index of described reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
In one embodiment, computing module 402, for utilizing symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index; Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
In one embodiment, computing module 402, for determining the recommendation number of users of described relevant to BRANDKEYS branding data, and determines the sub-index of the recommendation of the branding data relevant to BRANDKEYS based on described recommendation number of users; Determine the forwarding number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the forwarding of the branding data relevant to BRANDKEYS based on described forwarding number of users; Based on the weighted value of recommending sub-index and the sub-index of forwarding described in the weight calculation pre-set, the sub-index of described recommendation and the fertilizer index of weighted value as this BRANDKEYS forwarding sub-index.
In one embodiment, brand generic comprises the industry attributive classification information of this brand.
In one embodiment, branding data source comprises: news address, speech address, community or focus top news address.
Fig. 5 is the internet system architectural configurations figure of the evaluation of brands of embodiment of the present invention.
This system such as can comprise one or more client 1, one or more data storage server 3, one or more crawler server 2, and one or more arithmetic server 4.Although storage server 3, crawler server 2 and arithmetic server 4 are depicted as separate server in Figure 5, but according to an alternative embodiment of the invention, can only use a server to realize the function of storage server 3, crawler server 2 and arithmetic server 4.
Input (i.e. BRANDKEYS and brand generic) according to the user from client 1, crawler server 3 carries out information crawler, obtains internet content and/or relevant attribute information, and result is stored in data storage server 2.
Arithmetic server 2 visit data storage server 2, carries out text-processing, semantics identity, word frequency statistics and forwarding statistics to obtained internet content, thus determines fertilizer index, performance indicators and fertilizer index.
Arithmetic server 2 also calculates weighted sum using as evaluation of brands value based on fertilizer index, performance indicators and fertilizer index, and is stored to data storage server 2 by analyzing the evaluation of brands value drawn, and is supplied to client 1.
Client 1 can include, but are not limited to: functional mobile phone, smart mobile phone, palm PC, PC (PC), panel computer or personal digital assistant (PDA), etc.
Although enumerate the instantiation of client 1 above in detail, those skilled in the art can recognize, these are enumerated is only purposes of illustration, is not intended to limit the present invention the protection domain of embodiment.Client 1 goes for arbitrary intelligent terminal operation system, the operating system that specifically can adopt includes, but are not limited to: Android (Andorid), Palm OS, Symbian (Saipan), Windows mobile, Linux, Android (Android), iPhone (apple) OS, Black Berry (blackberry, blueberry) OS 6.0, Windows Phone series, etc.
Preferably, client 1 specifically can adopt Android operation system, and client 1 can use among each version of Andorid, include, but are not limited to: A Tongmu (Android Beta), clockwork spring robot (Android 1.0), cup cake (Android 1.5), baked donut (Android 1.6), muffin (Android2.0/2.1), frozen yogurt (Android 2.2), gingerbread (Android 2.3), honeycomb (Android 3.0), ice cream sandwich (Android 4.0), jelly beans (Jelly Bean, Android 4.1) etc. version.
Below enumerate the concrete version of Android platform in detail, it will be appreciated by those of skill in the art that embodiment of the present invention is not limited to and above-mentionedly enumerate version, and can also be applicable to other based among any version of Android software framework.
In fact, can specifically implement by various ways the evaluation of brands method and apparatus that embodiment of the present invention proposes.
Such as, the application programming interfaces of certain specification can be followed, evaluation of brands method is written as the plug-in card program be installed in PC, mobile terminal etc., also can be encapsulated as application program and download use voluntarily for user.When being written as plug-in card program, the multiple card format such as ocx, dll, cab can be implemented as.Also can implement by the concrete technology such as Flash plug-in unit, RealPlayer plug-in unit, MMS plug-in unit, MIDI staff plug-in unit, ActiveX plug-in unit the evaluation of brands method that embodiment of the present invention proposes.
The evaluation of brands method that embodiment of the present invention is proposed by the storing mode that can be stored by instruction or instruction set is stored on various storage medium.These storage mediums include, but are not limited to: floppy disk, CD, DVD, hard disk, flash memory, USB flash disk, CF card, SD card, mmc card, SM card, memory stick (MemoryStick), xD card etc.In addition, the evaluation of brands method that embodiment of the present invention can also be proposed is applied in the storage medium based on flash memory (Nand flash), such as USB flash disk, CF card, SD card, SDHC card, mmc card, SM card, memory stick, xD card etc.
In sum, in embodiments of the present invention, BRANDKEYS and brand generic is received; Determine branding data source relevant to brand generic in internet, and obtain the branding data relevant to BRANDKEYS from branding data source; Carry out exposure to the branding data relevant to BRANDKEYS to resolve with the performance indicators determining BRANDKEYS, text resolution is carried out to determine the value index of BRANDKEYS to the branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the branding data relevant to BRANDKEYS, to determine the fertilizer index of BRANDKEYS, and based on the weighted value of the weight calculation performance indicators, value index and the fertilizer index that pre-set.As can be seen here, embodiment of the present invention calculates evaluation of brands value based on performance indicators, value index and fertilizer index three dimensions, and be different from prior art and only weigh for price factor, therefore evaluation of brands value result of calculation is more accurate.
In addition, by the sub-index of exposure, the sub-index of cognition degree, the sub-index of reputation, the sub-index of degree of recognition, the assessed value of recommending sub-index, forwarding the many factors comprehensive assessment brands such as sub-index.Accurately can locate the problem place of abnormal brand.
The above, be only preferred embodiment of the present invention, be not intended to limit protection scope of the present invention.Within the spirit and principles in the present invention all, any amendment done, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (16)
1. an evaluation of brands method, is characterized in that, the method comprises:
Receive BRANDKEYS and brand generic;
Determine branding data source relevant to described brand generic in internet, and obtain the branding data relevant to described BRANDKEYS from described branding data source;
Carry out exposure to the described branding data relevant to BRANDKEYS to resolve with the performance indicators determining described BRANDKEYS, text resolution is carried out to determine the value index of described BRANDKEYS to the described branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the described branding data relevant to BRANDKEYS, to determine the fertilizer index of described BRANDKEYS, and based on the weighted value of performance indicators, value index and fertilizer index described in the weight calculation pre-set.
2. method according to claim 1, is characterized in that, describedly determines branding data source relevant to brand generic in internet, and obtains the branding data relevant to BRANDKEYS from described branding data source and comprise:
Determine vertical source of media relevant to described brand generic in internet;
Obtain with directed reptile mode or wide area reptile mode the media data comprising described BRANDKEYS from described vertical source of media.
3. method according to claim 1, is characterized in that, described to the branding data relevant to BRANDKEYS carry out exposure resolve with the performance indicators determining described BRANDKEYS, comprising:
Determine the sub-index of history exposure of described BRANDKEYS and the sub-index of cognition degree of described BRANDKEYS;
Based on the weighted value of the sub-index of history exposure and the sub-index of cognition degree described in the weight calculation pre-set, the weighted value of the sub-index of described history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
4. method according to claim 1, is characterized in that, carries out text resolution to determine the value index of BRANDKEYS, comprising the described branding data relevant to BRANDKEYS:
Semantics identity is carried out to the described branding data relevant to BRANDKEYS, and determines based on semantics identity result the sub-index of reputation described BRANDKEYS being presented to front assessment;
Pageview statistics is carried out to the described branding data relevant to BRANDKEYS, and determine to browse based on pageview statistics the number of users that branding data that described BRANDKEYS is correlated with exceedes predetermined threshold, and based on the sub-index of described number of users determination degree of recognition;
Based on the weighted value of the sub-index of reputation and the sub-index of degree of recognition described in the weight calculation pre-set, the weighted value of the sub-index of described reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
5. method according to claim 4, is characterized in that, describedly carries out semantics identity to the branding data relevant to BRANDKEYS and comprises:
Utilize symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index;
Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
6. method according to claim 1, is characterized in that, describedly carries out forwarding behavior parsing to the branding data relevant to BRANDKEYS, to determine that the fertilizer index of described BRANDKEYS comprises:
Determine the recommendation number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the recommendation of the branding data relevant to BRANDKEYS based on described recommendation number of users;
Determine the forwarding number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the forwarding of the branding data relevant to BRANDKEYS based on described forwarding number of users;
Based on the weighted value of recommending sub-index and the sub-index of forwarding described in the weight calculation pre-set, the sub-index of described recommendation and the fertilizer index of weighted value as this BRANDKEYS forwarding sub-index.
7. method according to claim 1, is characterized in that, described brand generic comprises the industry attributive classification information of this brand.
8. method according to claim 1, is characterized in that, described branding data source comprises: news address, speech address, community or focus top news address.
9. an evaluation of brands device, is characterized in that, this device comprises:
Receiver module, for receiving BRANDKEYS and brand generic;
Data acquisition module, for determining branding data source relevant to described brand generic in internet, and obtains the branding data relevant to described BRANDKEYS from described branding data source;
Computing module, resolve for carrying out exposure to the described branding data relevant to BRANDKEYS with the performance indicators determining described BRANDKEYS, text resolution is carried out to determine the value index of described BRANDKEYS to the described branding data relevant to BRANDKEYS, forwarding behavior parsing is carried out to the described branding data relevant to BRANDKEYS, to determine the fertilizer index of described BRANDKEYS, and based on the weighted value of performance indicators, value index and fertilizer index described in the weight calculation pre-set.
10. device according to claim 9, is characterized in that,
Data acquisition module, for determining vertical source of media relevant to described brand generic in internet; Obtain with directed reptile mode or wide area reptile mode the media data comprising described BRANDKEYS from described vertical source of media.
11. devices according to claim 9, is characterized in that,
Computing module, for the sub-index of cognition degree of the sub-index of history exposure and described BRANDKEYS of determining described BRANDKEYS; Based on the weighted value of the sub-index of history exposure and the sub-index of cognition degree described in the weight calculation pre-set, the weighted value of the sub-index of described history exposure and the sub-index of cognition degree is as the performance indicators of this BRANDKEYS.
12. devices according to claim 9, is characterized in that,
Computing module, for carrying out semantics identity to the described branding data relevant to BRANDKEYS, and determines based on semantics identity result the sub-index of reputation described BRANDKEYS being presented to front assessment; Pageview statistics is carried out to the described branding data relevant to BRANDKEYS, and determines to browse based on pageview statistics the number of users that branding data that described BRANDKEYS is correlated with exceedes predetermined threshold, than can based on the sub-index of number of users determination degree of recognition; Based on the weighted value of the sub-index of reputation and the sub-index of degree of recognition described in the weight calculation pre-set, the weighted value of the sub-index of described reputation and the sub-index of degree of recognition is as the performance indicators of this BRANDKEYS.
13. devices according to claim 12, is characterized in that,
Computing module, for utilizing symbol cutting for character string the original sampling data preset, and from the character string cut out, extract language linear structure and language block, respectively row is carried out down to the language linear structure extracted and language block, create language linear structure subindex and language block subindex, and language linear structure subindex and language block subindex are merged, to form overall index; Language linear structure and the language block of described branding data is extracted from the branding data relevant to BRANDKEYS, language linear structure and the language block of branding data described in this is retrieved respectively in described overall index, to determine language linear structure corresponding with the language linear structure of branding data described in this in overall index, and determine language block corresponding with the language block of this input of character string in described branding data, and obtain semantics identity result based on determined language linear structure and language block.
14. devices according to claim 9, is characterized in that,
Computing module, for determining the recommendation number of users of described relevant to BRANDKEYS branding data, and determines the sub-index of the recommendation of the branding data relevant to BRANDKEYS based on described recommendation number of users; Determine the forwarding number of users of described relevant to BRANDKEYS branding data, and determine the sub-index of the forwarding of the branding data relevant to BRANDKEYS based on described forwarding number of users; Based on the weighted value of recommending sub-index and the sub-index of forwarding described in the weight calculation pre-set, the sub-index of described recommendation and the fertilizer index of weighted value as this BRANDKEYS forwarding sub-index.
15. devices according to claim 9, is characterized in that, described brand generic comprises the industry attributive classification information of this brand.
16. devices according to claim 9, is characterized in that, described branding data source comprises: news address, speech address, community or focus top news address.
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