KR20170095492A

KR20170095492A - Method AND SYSTEM for Selecting A related keyword Group of Blog Marketing Based on Keyword

Info

Publication number: KR20170095492A
Application number: KR1020160016921A
Authority: KR
Inventors: 최성자; 김영학; 손민영
Original assignee: 금오공과대학교 산학협력단; 최성자
Priority date: 2016-02-15
Filing date: 2016-02-15
Publication date: 2017-08-23
Also published as: KR101791418B1

Abstract

A method for calculating the competitiveness of a related keyword (_i) includes the step of calculating the competitiveness value (CV(_i)) of the related keyword which is proportional to the monthly search amount (AS(_i)) of the related keyword (_i), is inversely proportional to the number of blogs (NB(_i)) including the related keyword (_i), and is proportional to a difference value (ADB(_i)) between a current date and an average writing date of the blog including the related keyword (_i). Accordingly, the present invention can select an efficient related keyword in consideration of the search amount and high rank exposure possibility.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and system for selecting a related keyword for keyword-based blog marketing,

The present invention relates to keyword selection for keyword-based blog marketing, and more particularly, to a related keyword selection method and a selection system for keyword-based blog marketing, and a competitive power calculation method and a calculation system of the related keyword.

Recently, the influence of SNS (Social Network Service) and online media is rising along with the spread of smartphones.

In particular, blogs based on keyword search portal are used not only as social network functions but also as a source of information and marketing means of companies, beyond simple personal information records.

According to the analysis of the impact of blog marketing, blogs are analyzed to analyze the fact that blogs have a larger ripple effect than comparatively low cost and give a priesthood to consumers. As a result of surveying the most effective services of digital marketing, blogs ranked first (34%).

One of the main reasons for the high influence of blogs is that blog information is exposed to the top in the search results of portal sites. This is the result of proving the informativeness of the blog. Naver, the largest portal site in Korea, provides a keyword-based search service, which uses a blog search system to expose highly relevant blogs as search results.

As the utilization of blog marketing increases, efforts are being made to analyze the blog search system of a large portal site and to be exposed at the top of keyword search keyword search. Recently, large corporations are developing strategies to promote their products by utilizing large amounts of influential blog information in portal search results.

The cost of marketing is further increased in order to make the top exposure of the portal site search result of the competitive keyword. Even if the keywords are paid for at a high cost and temporarily exposed to competitive keywords, the search results of popular keywords will be changed frequently due to the preference of the latest information, so it is difficult to expect a long-term marketing effect.

For reference, Korean Patent Registration No. 10-0751560 entitled " Keyword Advertisement Analysis System "proposes a system that presents optimized keywords in terms of cost.

On the other hand, some companies, such as SMEs and small merchants, are trying to run their own blogs, reduce marketing costs, and provide information on products. However, since there are about 10 million blogs currently in operation, it is difficult to expose relevant information to keyword search results on portal sites.

For top exposure, the value of the blog itself, as well as its relevance to the keyword, affects keyword search results. This is called the blog index, which usually requires at least a few months of long-term, regular effort to get a high blog index. Even if you have a high blog index, it is difficult to actively cope with search results that fluctuate from day to day in the case of highly competitive keywords.

On the other hand, the search volume of the keyword and the number of the blogs included in the search result of the keyword are not always proportional. One of the basic purposes of the blog is the personal record, and the keyword search purpose and the blog operation purpose always meet It is because there is not.

Blog (blog), a compound word of Web (Web) and Log (Journal), refers to a journal recorded by an individual on the Web. The number of domestic operating blogs is estimated to be about 10 million, which was used mainly for the purpose of establishing personal records or social networks in the past.

A large portal site also acknowledges the informativeness of blogs and exposes them to search results. As a result, blogs are becoming popular with introduction to products and services of companies, and marketing strategies in the form of late reviews. The information of ZyLog showed higher reliability and delivery power than other digital marketing.

Web search algorithms have been proposed using various methods for evaluating the importance of web documents and determining rankings. Among them, the PageRank algorithm applied to the Google search engine proposes a method of calculating the weight through hyperlinks between documents and is used as the basic theory of most search engines at present.

However, due to the nature of blogs, the frequency of use of hyperlinks is significantly lower than that of ordinary web pages, and the web search algorithm is not suitable for blog search. Thus, a blog ranking algorithm considering the features of blogs has been proposed. The blog ranking algorithm based on PageRank algorithm analyzed similarity and connectivity between blogs instead of hyperlinks.

Also, the B2Rank algorithm considers the frequency of blogging and the number of comments, and the EigenRumor algorithm applies the attractiveness of the blog and the user 's evaluation numerically. In addition, a method to determine blog ranking by numerical scrapping, trackback, etc. Has been proposed. Most blog search algorithms are configured to analyze the relevance of the value of the blog itself and the search keyword.

Therefore, for the top exposure in blog search, active long-term effort is required for the value of the blog itself as well as its relevance to the keyword.

Korean Patent Registration No. 10-0751560

SUMMARY OF THE INVENTION The present invention has been proposed in order to solve the above technical problems, and provides a method and a system for selecting related keywords for keyword-based blog marketing.

Also, it provides a method and system for calculating the competitiveness of related keywords for keyword based blog marketing.

According to an embodiment of the present invention, there is provided a method of selecting an associated keyword for keyword-based blog marketing, the method comprising: collecting search results of a portal site for a target keyword; Extracting a word of the word; According as the given keyword weight for a selected one of a plurality of word extraction associated keywords (ω _i), normalized keywords for the usage count of the document (D) all keywords used many times over a selected related keywords (ω _i) of (ω _j) in the Of the related keyword (? _I ) based on the difference value between the number N of the entire documents D and the number n of the documents D containing the selected related keywords? _I , And a calculation step of calculating an inverse number (IDF) of the document frequency indicating the sparseness and calculating an inverse number (IDF) of the normalized keyword frequency (TF) and a document frequency to calculate a related keyword weight value (IF-IDF) A selection method is provided.

The related keyword weight value (IF-IDF) is calculated by multiplying the normalized keyword frequency (TF) by the inverse number (IDF) of the document frequency.

The normalized keyword frequency (TF) is defined by the following equation (1).

&Quot; (1) "

freq (ω _i , D): the frequency of use of a particular word (ω _i ) in the document (D)

The inverse number (IDF) of the document frequency is defined by the following equation (2).

&Quot; (2) "

The associated keyword weight value (IF-IDF) is defined as Equation (3).

&Quot; (3) "

According to another embodiment of the present invention, there is provided a related keyword selection system for keyword-based blog marketing, which collects search results of a portal site for a target keyword, A word extracting unit for extracting a plurality of displayed words; And a selected one of a plurality of word extraction associated keyword weighting unit whether to grant the keyword weight of (ω _i); including, wherein the weighting unit includes a document (D) all keywords (ω _j) used many times over a selected associations in The normalized keyword frequency TF for the use frequency of the keyword ω _i is calculated and the number n of the documents D containing the number N of the entire documents D and the selected related keywords ω _i , IDF of the document frequency indicating the scarcity of the related keyword ω _i is calculated based on the difference value between the normalized keyword frequency TF and the document frequency IDF to calculate the associated keyword weight IF- ) Is calculated based on the keyword information.

Further, according to another embodiment of the invention, the associated keywords (ω _i) Monthly searches (AS (ω _i)) is proportional and associated keywords blog number (NB (ω _i) containing the (ω _i) of the ) in inverse proportion, and, associated with the keyword (mean creation date of containing the ω _i) blog to the difference value (ADB (ω _i of the current date) for calculating a) competitive value of the associated keyword (CV (ω _i) proportional to) the A method for calculating the competitiveness of an associated keyword, comprising:

Further, the competitive value CV (? _I ) of the associated keyword is defined as Equation (4).

&Quot; (4) "

Further, according to another embodiment of the invention, the associated keywords (ω _i) Monthly searches (AS (ω _i)) is proportional and associated keywords blog number (NB (ω _i) containing the (ω _i) of the ) in inverse proportion, and, associated with the keyword (mean creation date of containing the ω _i) blog to the difference value (ADB (ω _i of the current date) for calculating a) competitive value of the associated keyword (CV (ω _i) proportional to) the And a competitiveness calculating unit.

According to the related keyword selection method and the selection system for keyword-based blog marketing according to the embodiment of the present invention and the competitive power calculation method and the calculation system of the related keyword, efficient keyword can be selected considering the search volume and the possibility of high exposure have.

In addition, it is possible to extract related keywords having high relevance to the required target keywords, analyze the search volume and competitiveness of each keyword, and recommend the most efficient related keywords.

Also, when blogging for marketing, you can select keywords that are similar to your target keywords, so you can recommend keywords that are competitive and appealing to the top exposure.

1 is a diagram illustrating a related keyword selection method according to an embodiment of the present invention;
2 is a block diagram of a related keyword selection system according to an embodiment of the present invention.
3 is a diagram illustrating a competitive power calculation method of a related keyword according to an embodiment of the present invention;
FIG. 4 is a block diagram of a competitive power calculation system for a related keyword according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, in order to facilitate a person skilled in the art to easily carry out the technical idea of the present invention.

1 is a diagram illustrating a related keyword selection method according to an embodiment of the present invention.

The related keyword selection method (1) according to the present embodiment includes only a brief configuration for clearly explaining the technical idea to be proposed.

Referring to FIG. 1, the related keyword selection method (1) includes a word extraction step (S10) and a weight calculation step (S20).

In other words, in the related keyword selection method for keyword-based blog marketing (1)

First, a step of collecting search results of a portal site for a target keyword and extracting a plurality of words displayed on the collected document D through a Korean morphological analysis is performed. - S10 -

Next, in assigning the keyword weight of the selected related keyword (? _I ) among the plurality of extracted words,

_Calculates a normalized keyword frequency (TF) with respect to the number of times of use of the associated keyword (? _I ) selected from the number of times of use of all the keywords (? _J ) in the document (D)

The whole document (D) the number (N) and the selected associated keywords inverse of the document frequency indicating the rarity of keywords (ω _i) associated on the basis of the difference value of the number (n) of the document (D) including the (ω _i) of ( IDF)

A calculation step of calculating the associated keyword weight value IF-IDF by calculating the normalized keyword frequency TF and the inverse number IDF of the document frequency is performed. - S20 -

At this time, the associated keyword weighting value (IF-IDF) can be calculated by multiplying the normalized keyword frequency (TF) by the inverse number (IDF) of the document frequency.

The normalized keyword frequency (TF) can be defined by the following equation (1).

&Quot; (1) "

In addition, the inverse number (IDF) of the document frequency can be defined by the following equation (2).

&Quot; (2) "

That is, for the related keyword extraction, the document contents of the portal site keyword search result are collected, and the nouns used are extracted through the morphological analysis of Hangeul. The extracted noun words are used to calculate the frequency to determine the importance.

The related keyword weight (IF-IDF) is used as a weight calculation method for determining importance.

First, the normalized keyword frequency (TF) calculates the weight of a specific word (? _I ) in the document (D) through the frequency (freq (? _I , D)). However, since the frequency of the document increases as the content of the document increases, the normalized keyword frequency (TF), which is obtained by dividing the frequency by the total number of words, can be used.

&Quot; (1) "

Next, the Inverse Document Frequency (IDF) calculates the scarcity and informationality of a word using the document frequency inverse number. If the total number of documents in the blog search result is N and the number of documents containing a specific word (? _I ) is n, the inverse number (IDF) of the document frequency can be obtained as follows.

&Quot; (2) "

Next, the associated keyword weighting value (IF-IDF) can be obtained as follows by calculation of the normalized keyword frequency (TF) and the inverse number of the document frequency (IDF).

&Quot; (3) "

2 is a configuration diagram of an associated keyword selection system 2 according to an embodiment of the present invention. Fig. 2 is defined as a system for performing the related keyword selection method (1) of Fig.

Referring to FIG. 2, the related keyword selection system 2 includes a word extracting unit 10 and a weight assigning unit 20.

In other words, in the related keyword selection system for keyword-based blog marketing (2)

The word extracting unit 10 collects the search results of the portal site for the target keyword and extracts a plurality of words displayed in the collected document D through the Korean morphological analysis.

In addition, the weight assigning unit 20 assigns a keyword weight of the selected related keyword (? _I ) among the plurality of extracted words.

In particular, the weight assigning unit 20

The related keyword weight value IF-IDF is calculated by calculating the normalized keyword frequency TF and the inverse number IDF of the document frequency.

As described above, the related keyword weight value IF-IDF can be calculated by multiplying the normalized keyword frequency TF by the inverse number IDF of the document frequency.

&Quot; (1) "

&Quot; (2) "

To extract related keywords, document contents of portal site keyword search results are collected and nouns are extracted through morphological analysis of Hangul. The extracted noun words are used to calculate the frequency to determine the importance.

&Quot; (1) "

&Quot; (2) "

&Quot; (3) "

On the other hand, the competitiveness of the associated keyword (? _I ) can be calculated after the related keyword (? _I ) is selected through the above-described method.

FIG. 3 is a diagram illustrating a competitive power calculation method of a related keyword according to an embodiment of the present invention.

Referring to FIG. 3, the competitive power calculation method (3) of the related keyword includes a step (S30) of selecting a related keyword (? _I ) and a step (S40) of calculating a competitive power value.

That is, in the competitive power calculation method (3) of the selected related keyword (? _I )

Step (S30) for selecting the priority, associated keywords (ω _i) may be selected for the associated keywords (ω _i) by the method and system of the above-described FIGS. - S30 -

Next, in proportion to the monthly retrieval amount AS (? _I ) of the related keyword? _I ,

Is inversely proportional to the number of blogs NB (? _I ) including the related keyword? _I ,

An association step for calculating a keyword (ω _i) competitive value of the associated keyword that is proportional to the difference (ADB (ω _i)) of the average creation date and the current date of the blog contains (CV (ω _i)) are in progress. - S40 -

The competitive value CV (? _I ) of the associated keyword can be defined as shown in Equation (4) below.

&Quot; (4) "

4 is a configuration diagram of a competitive keyword computing system 4 according to an embodiment of the present invention. 4 is defined as a system 4 that carries out the competition calculation method (3) of the associated keyword of Fig.

Referring to FIG. 4, the competitiveness calculation system 4 of the related keyword includes an associated keyword selection unit 30 and a competitive power calculation unit 40.

First, the associated keyword selection unit 30 can select the related keyword? _I through the above-described method and system of FIG. 1 and FIG.

Next, the competitiveness calculator 40 calculates the competitiveness calculator 40 in proportion to the monthly retrieval amount AS (? _I ) of the associated keyword? _I ,

Associated calculates the keyword (ω _i) competitive value of the associated keyword that is proportional to the difference (ADB (ω _i)) of the average creation date and the current date of the blog contains (CV (ω _i)).

&Quot; (4) "

Certain associated keyword competitiveness (CV (ω _i)) for the measurement and monthly search volume (AS (ω _i)) and blog number (NB (ω _i)), and higher exposure to blogs 10 suggests the average date of the (ω _i) And the difference value ADB (? _I ) between the current date and the current date.

The related keywords (ω _i ) are recommended in reverse order of the calculated competitiveness values.

As the monthly search volume (AS (ω _i )) is higher, the marketing blog is more frequently exposed, so it is advantageous for the competitive value (CV (ω _i )) and the smaller the blog number (NB (ω _i ) It is highly competitive because it is advantageous for the top exposure of blog.

The monthly search volume is associated with the total number of exposures, which can be compensated by using multiple keywords with a small search volume. However, as the number of blogs included in the searched keywords increases, the number of top exposures rapidly deteriorates. Therefore, it is necessary to be treated as an important factor as compared with the monthly searched amount because the number of keywords in the blog can not be solved.

The higher the difference value ADB (? _I ) between the average creation date and the current date of the top exposure block of the keyword is advantageous to the competitive value CV (? _I ). The keywords of frequently created blogs are disadvantageous to competitiveness because they are frequently replaced even if the exposure is high. The fact that the blogs created in the past are the top exposures can be attributed to the number of blogging or the number of blogging using the keywords, which is advantageous for competitiveness.

Tables 1 through 5 illustrate related groups of keywords for loans, marriages, moving, toys, travel, and their competitiveness through the methods and systems described above.

Target keyword Order of recommendation Related Keywords Monthly Search Volume Number of blogs Competitiveness loan - loan 120,086 1,763,633 3.9E-06 loan One sudden change 8,927 48,330 1.1E-03 loan 2 Tremor 2,576 55,160 8.5E-05 loan 3 guarantor 3,174 67,620 6.9E-05 loan 4 High interest rate 586 68,101 1.3E-05 loan 5 Repayment 11,763 408,830 7.0E-06 loan 6 Interest rate 47,281 926,519 5.5E-06

Referring to Table 1, it can be seen that the related keywords (ω _i ) for the target keyword "loan" have high competitiveness in the order of feed, loan, guarantor, high interest rate, repayment, and interest rate.

Target keyword Order of recommendation Related Keywords Monthly Search Volume Number of blogs Competitiveness marriage - marriage 54,769 7,383,699 1.0E-07 marriage One invitation 99,419 208,543 2.3E-04 marriage 2 Coupling 209,977 419, 554 1.2E-04 marriage 3 robe 10,716 124,767 6.9E-05 marriage 4 Gift 20,140 424,577 1.1E-05 marriage 5 Compatibility 39,182 1,082,286 3.3E-06 marriage 6 A ceremony 338 206,227 7.9E-07 marriage 7 dress 31,782 2,204,505 6.5E-07 marriage 8 priest 7,107 2,478,733 1.2E-07

Referring to Table 2, it can be seen that the related keywords (ω _i ) for the target keyword "marriage" are highly competitive in order of wedding invitation, coupling, robes, gifts, compatibility, wedding ceremony, dress, and priest.

Target keyword Order of recommendation Related Keywords Monthly Search Volume Number of blogs Competitiveness move - move 34,413 4,529,977 1.7E-07 move One Dragon 25,411 234,335 4.6E-05 move 2 one room 34,287 2,014,943 8.4E-07 move 3 estimate 5,879 1,466,440 2.7E-07

Referring to Table 3, it can be confirmed that the related keywords (ω _i ) for the "target" keyword "moving" have a high competitive power in the order of drag, one room, and quote.

Target keyword Order of recommendation Related Keywords Monthly Search Volume Number of blogs Competitiveness toy - toy 30,533 2,028,374 7.4E-07 toy One Turning 4,270 132,024 2.4E-05 toy 2 Toys 39,500 442,055 2.0E-05 toy 3 dinosaur 74,168 733,334 1.4E-05 toy 4 child 83,456 1,690,731 2.9E-06 toy 5 robot 26,931 988,760 2.8E-06

Referring to Table 4, it can be confirmed that the related keyword (ω _i ) for the target keyword "toy" has a high competitive power in the order of turning, toy, dinosaur, infant, and robot.

Target keyword Order of recommendation Related Keywords Monthly Search Volume Number of blogs Competitiveness Travel - Travel 98,658 17,294,788 3.3E-08 Travel One Jeju Island 223,909 2,831,506 2.8E-06 Travel 2 autumn 296,846 9,069,883 3.6E-07 Travel 3 package 11,086 1,933,936 3.0E-07 Travel 4 Sea 102,153 8,007,881 1.6E-07 Travel 5 hotel 50,963 5,686,388 1.6E-07 Travel 6 airport 15,454 3,558,133 1.2E-07 Travel 7 signal 3,229 1,885,132 9.1E-08

Referring to Table 5, it can be seen that the related keywords (ω _i ) for the target keyword "travel" are highly competitive in the order of Jeju Island, autumn, package, sea, hotel, airport, and newlywed.

In the embodiment of the present invention, in order to retrieve the related keywords, morphological analysis is performed on the contents of 100 documents of the top blog, and the degree of association is calculated based on the frequency of the keywords of the nouns or compound nouns.

Thus, those skilled in the art will appreciate that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. It is therefore to be understood that the embodiments described above are to be considered in all respects only as illustrative and not restrictive. The scope of the present invention is defined by the appended claims rather than the detailed description and all changes or modifications derived from the meaning and scope of the claims and their equivalents are to be construed as being included within the scope of the present invention do.

10: word extracting unit
20: Weight assignment
30: Associative keyword selection unit
40: Competitiveness Calculator

Claims

In a related keyword selection method for keyword-based blog marketing,
Collecting search results of a portal site for a target keyword, and extracting a plurality of words displayed on the collected document (D) through a Korean morphological analysis;
In assigning the keyword weight of the selected related keyword (? _I ) among the plurality of extracted words,
_Calculates a normalized keyword frequency (TF) with respect to the number of times of use of the associated keyword (? _I ) selected from the number of times of use of all the keywords (? _J ) in the document (D)
The whole document (D) the number (N) and the selected associated keywords inverse of the document frequency indicating the rarity of keywords (ω _i) associated on the basis of the difference value of the number (n) of the document (D) including the (ω _i) of ( IDF)
A calculation step of calculating an associated keyword weight value IF-IDF by calculating a normalized keyword frequency (TF) and an inverse number of a document frequency (IDF);
The method comprising:

The method according to claim 1,
Wherein the related keyword weighting value (IF-IDF) is calculated by multiplying a normalized keyword frequency (TF) by an inverse number (IDF) of the document frequency.

The method according to claim 1,
Wherein the normalized keyword frequency (TF) is defined as: " (1) "
&Quot; (1) "

The method according to claim 1,
Wherein the inverse number (IDF) of the document frequency is defined by the following equation (2).
&Quot; (2) "

The method according to claim 1,
Wherein the related keyword weighting value (IF-IDF) is defined as Equation (3).
&Quot; (3) "

In a related keyword selection system for keyword-based blog marketing,
A word extracting unit for collecting search results of a portal site for a target keyword and extracting a plurality of words displayed in the collected document D through a Korean morphological analysis; And
And a weight assigning unit for assigning a keyword weight of the selected related keyword (? _I ) among the plurality of extracted words,
The weighting unit may include:
_Calculates a normalized keyword frequency (TF) with respect to the number of times of use of the associated keyword (? _I ) selected from the number of times of use of all the keywords (? _J ) in the document (D)
The whole document (D) the number (N) and the selected associated keywords inverse of the document frequency indicating the rarity of keywords (ω _i) associated on the basis of the difference value of the number (n) of the document (D) including the (ω _i) of ( IDF)
Wherein the related keyword weighting unit (IF-IDF) is calculated by calculating the normalized keyword frequency (TF) and the inverse number of the document frequency (IDF).

The method according to claim 6,
Wherein the related keyword weighting value (IF-IDF) is calculated by multiplying a normalized keyword frequency (TF) by an inverse number (IDF) of a document frequency.

The method according to claim 6,
Wherein the normalized keyword frequency (TF) is defined as: " (1) "
&Quot; (1) "

The method according to claim 6,
Wherein the inverse number of the document frequency (IDF) is defined by the following equation (2).
&Quot; (2) "

The method according to claim 6,
Wherein the related keyword weighting value (IF-IDF) is defined as Equation (3).
&Quot; (3) "

In the competitive power calculating method of the related keyword (? _I )
Relative to the associated keyword monthly search volume (AS (ω _i)) of (ω _i), and
Is inversely proportional to the number of blogs NB (? _I ) including the related keyword? _I ,
Association step for calculating a keyword (ω _i) competitive value of the associated keyword that is proportional to the average difference value and the creation date (ADB (ω _i)) for the current day of the blog that contains (CV (ω _i));
The method comprising the steps of:

12. The method of claim 11,
Wherein the competitive value CV (? _I ) of the associated keyword is defined as Equation (4).
&Quot; (4) "

In the competitive power calculation system of the related keyword (? _I )
Relative to the associated keyword monthly search volume (AS (ω _i)) of (ω _i), and
Is inversely proportional to the number of blogs NB (? _I ) including the related keyword? _I ,
Associate keywords mean the creation date of the blog that contains the (ω _i) and the calculated competitive for calculating the value of the associated competitive keyword that is proportional to the difference (ADB (ω _i)) for the current day (CV (ω _i)) section;
Wherein the system further comprises:

14. The method of claim 13,
Wherein the competitive value CV (? _I ) of the associated keyword is defined as Equation (4).
&Quot; (4) "