KR20190040700A - Device and method for expressing urban identity using hashtag - Google Patents

Abstract

The present invention relates to an apparatus for expressing a city identity using a hashtag, and to a method thereof. The apparatus for expressing a city identity using a hashtag collects hashtag keywords related to a city or a specific location of the city on a social network service to classify the hashtag keywords per category; and calculates and adds frequency numbers for each category to express a graphic corresponding to the frequency numbers on a map, thereby allowing people without prior information to easily figure out the city.

Description

Technical Field [0001] The present invention relates to a device and method for expressing urban identity using a hash tag,

The present invention relates to an apparatus and method for expressing urban identity using a hash tag, and more particularly, to a method and apparatus for collecting hash tag keywords related to specific locations of a city or a city on a social network service and classifying the hash tag keywords into categories, And more particularly, to an apparatus and method for expressing urban identity using a hash tag that allows a person who does not have prior information to easily grasp a city by calculating and adding a frequency and displaying a graphic corresponding to the frequency on a map.

Attempts have been made to identify urban identity for sustainable development of the city. Here, urban identity is defined as a Gestalt type concept consisting of physical specificity, social image possibility, emotional attachment and satisfaction. This concept has been modified in the physical location, activity, and meaning of the urban identity proposed by Relph. Thus, urban identity can manifest itself as a physical peculiarity and a subjective meaning of the city.

Korean Patent Publication No. 10-2015-0126289 Korean Patent No. 10-1713558

The present invention collects hash tag keywords related to specific locations of a city or a city on a social network service, classifies the hash tag keywords according to categories, calculates and adds frequency numbers for each category, and displays a graphic corresponding to the frequency on a map The present invention also provides an apparatus and method for expressing urban identity using a hash tag that allows people without prior information to easily grasp the city.

An urban identity expression apparatus using a hash tag for realizing the object of the present invention searches for a location by using a search engine of a social network service (SNS), and stores the data of a hash tag (Hashtag) A collecting unit for collecting the collected objects; A first extracting unit for extracting the data as a metadata of a CSV (Comma Separated Value) file; A second extracting unit for extracting a hash tag keyword in a caption text column of the metadata; A removal unit configured to remove the stopwords related to the social network service from the hashtag keyword; A classification unit for classifying the hash tag keywords in which the stopwords have been removed, by category; A calculation unit for counting the hashtag keywords from which the stopwords have been removed to calculate a frequency, and adding the frequencies of the hashtag keywords according to the categories; A graphical unit for forming a concentric circle having a radius corresponding to each frequency in each of the categories and representing the concentric circle in a color designated according to the category; And an overlap expression unit for overlapping the concentric circle with the map on which the position appears.

The collecting unit may include a first calculating unit for searching a city using a search engine of a social network service, collecting data of a hash tag including the location, and calculating a frequency of the location text; And a second calculation unit for calculating a check-in point density of the location in the city on the social network service.

The collecting unit may further include a setting unit that sets the location of the city as a search target when the frequency of the location text or the check-in point density of the location is a predetermined threshold frequency or a predetermined threshold density or more.

On the other hand, the second extracting unit may exclude un-tagged text.

The classifying unit may delete the category when the frequency of the category is less than a predetermined threshold frequency.

A method of representing a city identity using a hash tag for realizing the object of the present invention includes the steps of: (a) searching for a location using a search engine of a social network service and collecting data of a hash tag including a location; (b) extracting the data as metadata of a CSV file; (c) extracting a hash tag keyword in a caption text string of the metadata; (d) performing removal of an abbreviation relating to the social network service from the hashtag keyword; (e) classifying the hash tag keywords from which the stopwords have been removed, by category; (f) counting the hash tag keywords from which the stopwords are removed, calculating a frequency, and summing the frequencies of the hash tag keywords for each category; (g) forming a concentric circle having a radius corresponding to each frequency of each of the categories, and expressing the concentric circle in a color designated according to the category; And (h) overlapping the concentric circle with the map on which the location appears.

The step (a) may include searching a city using a search engine of a social network service, collecting data of a hash tag including the location, and calculating a frequency of the location text; And computing the check-in point density of the location in the city on the social network service.

The step (a) may set the location of the city as a search target when the frequency of the location text or the check-in point density of the location is a predetermined threshold frequency or a predetermined threshold density or more.

Meanwhile, in the step (c), untagged text may be excluded.

The step (e) may delete the category when the frequency of the category is less than a predetermined threshold frequency.

According to the apparatus and method for representing city identity using a hash tag according to the present invention,

First, it is possible to set a specific position having important meaning in the city by utilizing the density of the check-in point or the frequency of the location text.

Second, the hash tag keywords on the social network service can be counted to calculate the frequency, and classified by category and summed up.

Third, a category having a frequency lower than a predetermined threshold can be deleted, and a city can be effectively expressed by highlighting a relatively more important category.

Fourth, by concentrating the concentric circles of the colors designated for each category, the city can be imaged so that people without prior information can easily recognize the city at a glance.

Fifth, since the size of each concentric circle radius is expressed in correspondence with the frequency of each category, people without prior information can easily grasp the identity of the city.

1 is a block diagram of an apparatus for representing an urban identity using a hash tag according to the present invention.
2 is a detailed block diagram of the collector shown in Fig.
3 is a flowchart illustrating an algorithm for representing a city identity using a hash tag according to the present invention.
4 is a detailed algorithm flow chart of the data collection step of the hash tag shown in FIG.
5 is a table in which hash tag keywords are classified according to categories and different colors are designated.
FIG. 6 is a table in which the hash tag keywords are classified according to categories, and the frequency numbers are calculated and added.
7 is a schematic diagram in which a concentric circle overlaps a map in which a position appears.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Note that, in the drawings, the same components are denoted by the same reference symbols as possible. Further, the detailed description of known functions and configurations that may obscure the gist of the present invention will be omitted. For the same reason, some of the components in the drawings are exaggerated, omitted, or schematically illustrated.

The 'user' referred to in the present invention means a user who uses a social network service.

One of the most common methods for identifying urban identity is to use check-in points on the SNS, and to identify spatially significant locations in the city by identifying the distribution of check-in points.

However, due to the unstable Wi-Fi connection and the psychological desire for SNS users to enjoy the atmosphere in the outdoor space, the city's important location identification technique utilizing the distribution of check-in points in the SNS can be used for real- And may instead be inaccurate due to behavioral patterns such as returning to the daily space and uploading photos using a hashtag.

In addition, surveying methods such as questionnaires, interviews, etc. were used to identify urban identities, as well as the distribution of check-in points at specific locations in the city. However, there is a problem in reliability as a subjective technique.

The social network service is Cyworld 's mini - homepage, blog, and smartphone, which is a way to communicate online with existing offline network connections, and Twitter Such as Tumblr, Pinterest, and Instagram, which are customized services that are shared around specific topics such as Twitter, Facebook, and interests and hobbies.

1 is a block diagram of an apparatus 1000 for representing an urban identity using a hash tag according to the present invention. 2 is a detailed block diagram of the collecting unit 100 shown in FIG.

1 and 2, an apparatus 1000 for representing an urban identity using a hash tag according to the present invention includes a collecting unit 100, a first extracting unit 200, a second extracting unit 300, 400, a classifier 500, an operation unit 600, a graphic unit 700, and an overlap expression unit 800.

The collection unit 100 searches for a location using a search engine of a novel network service, and collects data of a hash tag including a location.

Here, the location is a place that is often checked in by users on the social network service or uploaded with the hashtag on the social network, where the users of the social network service in the city are identified as important Lt; / RTI > A location may refer to a specific place in a particular city, such as a place name, a train station, a park, a department store, a street, and the like. In addition, a specific position in the city can be quantitatively set as described below.

For example, when a specific city such as a minute-by-minute is searched by a search engine of a social network service, the first calculation unit 110 may collect data of a hash tag including a location and calculate a frequency of the location text. At this time, the data of the hashtag including the location may be the user of the social network service uploading the specific location together with the specific city such as the minutes of the minute, using the hashtag, together with the hashtag on the novel network service, Text without hashtags may not be collected.

In addition, if a specific city such as the city of minutes is searched through a search engine of a social network service, the second calculation unit 120 calculates a check-in point density of a specific location such as a place name, a train station, a park, a department store, Can be calculated. Here, check-in is for a user of a social network service to inform his / her friends of his / her location at a specific location in a specific city such as Bundang City, and may be a function on a social network service.

The setting unit 130 sets the checkpoint density of the location text or the location of the location text using the frequency of the location text calculated by the first calculator 110 or the checkpoint density of the location calculated by the second calculator 120 When a predetermined threshold frequency or a predetermined threshold density or more, the location of a specific city can be set as a search target.

The collecting unit 100 sets a specific location of a particular city to be uploaded together with a hash tag on the social network in this way, and searches for a location in a search engine of a social network service, Collect.

The first extracting unit 200 extracts the data of the hash tag including the position into the metadata of the CSV file format. The metadata of the CSV file format may include creation date, caption text, latitude, longitude, and the like.

The second extracting unit 300 extracts a hash tag keyword from the caption text string of the metadata. Also, the second extracting unit 300 may exclude the untagged text from the caption text string of the metadata.

The removal unit 400 removes an abbreviation related to a social network service such as an instance, a tag, a follow, a good, and an outfit of the day (OOTD) from a hash tag keyword.

The classification unit 500 classifies the hash tag keywords of the lower concept from which the stopwords have been removed into categories of upper concept such as sightseeing, date, sports, culture art, meal, work, and the like. For example, sub-concept hashtag keywords such as drinks, beer, brunch, cafe, chicken, shochu, sushi, and Starbucks can be classified into categories of superior concepts such as meals, ballet, cycle, golf, Sub-concept hashtag keywords such as pilates, stretching, weight training, etc. can be classified into hashtags of the upper concept such as sports. In addition, sub-concept hash tag keywords such as bus kings, concerts, department store culture centers, hip-hop, movies, museums, etc. can be classified into categories of upper concept such as culture and art.

The operation unit 600 counts the hash tag keywords from which the stopwords are removed, calculates the frequency, and sums the frequencies of the hash tag keywords for each category. Here, the classifying section 500 can delete the category when the frequency of the category is less than the predetermined threshold frequency. For example, if the frequency of the tourism category is 5000, the frequency of the date category is 6000, and the category of the sports is 120, by designating the threshold frequency to 3000 and deleting the category of sports below the critical frequency, It is possible to effectively express the characteristic of the specific location by relatively highlighting the tourist and date category.

The graphic unit 700 forms a concentric circle having a radius corresponding to each frequency by category, and the concentric circle is expressed by a color designated according to the category. The color of concentric circles can be specified in the color list of the R software.

The overlap expression unit 800 overlaps the concentric circle on the map where the city including the position appears. The overlapped expression unit 800 may retrieve an existing map including a location set in the setting unit 130 of the collection unit 100. [

FIG. 3 is a flowchart of an algorithm for representing a city identity using a hash tag according to the present invention. Even when the city identity representation apparatus 1000 using the hashtag of FIG. 1 is implemented in a time-series manner, The first extracting unit 200, the second extracting unit 300, the removing unit 400, the classifying unit 500, the calculating unit 600, the graphic unit 700, and the overlap display unit 800 Fig. 4 is a detailed algorithm flowchart of the data collection step of the hash tag shown in Fig. 3, and a detailed block diagram of the collection unit shown in Fig. 1 of Fig. 2 is shown in a time-series manner The present invention is applied to the first calculation unit 110, the second calculation unit 120 and the setting unit 130 as they are in the present embodiment.

3 and 4, a method for representing a city identity using a hash tag according to the present invention includes a hash tag data collection step S100, a metadata extraction step S200, a hash tag keyword extraction step S300, (Step S400), a category classification step S500, a frequency calculation and summation step S600, a concentric circle formation and color designation step S700, and a step of overlapping concentric circles on the map S800.

In step S100, the collecting unit 100 searches for a location using a search engine of a social network service, and collects data of a hash tag including a location.

Here, the location is a place that is often checked in by users on the social network service or uploaded with the hashtag on the social network, where the users of the social network service in the city are identified as important Lt; / RTI > A location may refer to a specific place in a particular city, such as a place name, a train station, a park, a department store, a street, and the like. In addition, a specific position in the city can be quantitatively set as described below.

For example, if it is determined in step S110 that a specific city such as the city of minutes is searched by the search engine of the social network service, the first calculation unit 110 collects the data of the hash tag including the location to calculate the frequency of the location text . At this time, the data of the hashtag including the location may be the user of the social network service uploading the specific location together with the specific city such as the minutes of the minute, using the hashtag, together with the hashtag on the novel network service, Text without hashtags may not be collected.

In step S120, when a specific city such as a minute of the minute is searched through a search engine of the social network service, the second calculation unit 120 calculates a specific location such as a place name, a train station, a park, a department store, Check-in point density can be calculated. Here, check-in is for a user of a social network service to inform his / her friends of his / her location at a specific location in a specific city such as Bundang City, and may be a function on a social network service.

In step S130, the setting unit 130 sets the frequency or position of the position text using the frequency of the position text calculated by the first calculation unit 110 or the check-in point density of the position calculated by the second calculation unit 120 When the check-in point density is equal to or greater than a predetermined threshold frequency or a predetermined threshold density, the location of a specific city can be set as a search target. Accordingly, the collecting unit 100 in step S100 sets a specific location of a particular city to be uploaded with the hash tag on the social network in this manner, and when searching the location in the search engine of the social network service, Collect the data of the hashtag.

In step S200, the first extracting unit 200 extracts the data of the hash tag including the position into the metadata of the CSV file format. The metadata of the CSV file format may include creation date, caption text, latitude, longitude, and the like.

In step S300, the second extraction unit 300 extracts a hash tag keyword from the caption text string of the metadata. Also, the second extracting unit 300 may exclude the untagged text from the caption text string of the metadata.

In step S400, the removal unit 400 removes an idiom associated with a social network service such as an instance, a tag, a follow, a favorite, and Out of the Day (OOTD) from a hash tag keyword.

In step S500, the classifying unit 500 classifies the hash tag keywords of the lower concept from which the stopwords have been removed into categories of superior concept such as sightseeing, date, sports, culture art, meal, work, For example, sub-concept hashtag keywords such as drinks, beer, brunch, cafe, chicken, shochu, sushi, and Starbucks can be classified into categories of superior concepts such as meals, ballet, cycle, golf, Sub-concept hashtag keywords such as pilates, stretching, weight training, etc. can be classified into hashtags of the upper concept such as sports. In addition, sub-concept hash tag keywords such as bus kings, concerts, department store culture centers, hip-hop, movies, museums, etc. can be classified into categories of upper concept such as culture and art.

In operation S600, the operation unit 600 counts the hash tag keywords from which the stopwords are removed, calculates the frequency, and sums the frequencies of the hash tag keywords for each category. Here, the classifying section 500 can delete the category when the frequency of the category is less than the predetermined threshold frequency. For example, if the frequency of the tourism category is 5000, the frequency of the date category is 6000, and the category of the sports is 120, by designating the threshold frequency to 3000 and deleting the category of sports below the critical frequency, It is possible to effectively express the characteristic of the specific location by relatively highlighting the tourist and date category.

In step S700, the graphic unit 700 forms a concentric circle having a radius corresponding to each frequency in each category, and the concentric circle is expressed in a color designated according to the category. The color of concentric circles can be specified in the color list of the R software.

In step S800, the overram representation unit 800 overlaps the concentric circles on the map where the city including the location appears. The overlapped expression unit 800 may retrieve an existing map including a location set in the setting unit 130 of the collection unit 100. [

FIG. 5 is a table in which hashtag keywords are classified according to categories and different colors are designated. FIG. 6 is a table in which hashtag keywords are classified according to categories and frequency numbers are calculated and added. FIG. Fig.

5 to 7, subordinate hashtag keywords such as ballet, cycle, golf, fitness, pilates, and weight training can be classified into categories of superior concept such as sports / exercise and colors of red can be designated . In addition, the frequency of the hashtag keyword, Cafe, which is a sub-concept keyword classified into the upper concept meal category, is 1215, the frequency of beer is 808, the number of Drinks is 535, the number of Shake Shake is 304 , And the frequency of these hash tag keywords may be added to the frequency of the eating category.

By identifying the color and size of a particular concentric circle with a radius corresponding to the frequency of the category, a colored concentric circle overlapping a map containing a specific location, and a location having a significant meaning in a specific city, People without prior information can easily grasp it.

It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined in the appended claims. It will be possible.

1000: City identity expression device using hashtag
100: Collecting section
200: first extracting unit
300: second extracting unit
400: Remove
500:
600:
700: Graphic section
800: Overlap expression unit

Claims (10)

A collection unit for searching a location using a search engine of a social network service (SNS) and collecting data of a hash tag (Hashtag) including a location;
A first extracting unit for extracting the data as a metadata of a CSV (Comma Separated Value) file;
A second extracting unit for extracting a hash tag keyword in a caption text column of the metadata;
A removal unit configured to remove the stopwords related to the social network service from the hashtag keyword;
A classification unit for classifying the hash tag keywords in which the stopwords have been removed, by category;
A calculation unit for counting the hashtag keywords from which the stopwords have been removed to calculate a frequency, and adding the frequencies of the hashtag keywords according to the categories;
A graphical unit for forming a concentric circle having a radius corresponding to each frequency of the categories, the concentric circle having a color designated by a category; And
And an overlap representation unit for overlapping the concentric circle with a map on which the position is displayed. The method according to claim 1,
Wherein,
A first calculation unit for searching a city using a search engine of a social network service, collecting data of a hash tag including the location, and calculating a frequency of the location text; And
Further comprising a second calculation unit for calculating a check-in point density of the location in the city on the social network service. The method of claim 2,
Wherein,
Further comprising a setting unit configured to set the location of the city as a search target when the frequency of the location text or the check-in point density of the location is a predetermined threshold frequency or a predetermined threshold density or more Identity representation device. The method according to claim 1,
Wherein the second extracting unit comprises:
Wherein the un-tagged text is excluded from the un-tagged text. The method according to claim 1,
Wherein,
And when the frequency of the category is less than a predetermined threshold frequency, the category is deleted. (a) retrieving a location using a search engine of a social network service, and collecting data of a hash tag including a location;
(b) extracting the data as metadata of a CSV file;
(c) extracting a hash tag keyword in a caption text string of the metadata;
(d) performing removal of an abbreviation relating to the social network service from the hashtag keyword;
(e) classifying the hash tag keywords from which the stopwords have been removed, by category;
(f) counting the hash tag keywords from which the stopwords are removed, calculating a frequency, and summing the frequencies of the hash tag keywords for each category;
(g) forming a concentric circle having a radius corresponding to each frequency of each of the categories, and expressing the concentric circle in a color designated according to the category; And
(h) overlapping the concentric circle with a map on which the location is indicated. The method of claim 6,
The step (a)
Retrieving a city using a search engine of a social network service, collecting data of a hash tag including the location, and calculating a frequency of the location text; And
And calculating the density of the check-in point of the location in the city on the social network service. The method of claim 7,
The step (a)
Wherein the location of the city is set as a search target when the frequency of the location text or the check-in point density of the location is a predetermined threshold frequency or a predetermined threshold density or more. The method of claim 6,
The step (c)
And the untagged text is excluded. The method of claim 6,
The step (e)
And deleting the category when the frequency of the category is less than a predetermined threshold frequency.

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