KR101659374B1 - The maketing area generating system and the generating method thereof - Google Patents

Abstract

Embodiments relate to a communication unit for receiving a commerce image creation request from a user terminal of a specific user via a wired or wireless communication network, and a web crawler and a wrapper-based web crawler ) To collect data, and perform spam filtering of the collected data to generate the commercial image from the non-spam data. Therefore, by analyzing the big data, it is possible to improve the accuracy and satisfaction of the recommendation to the individual user by recommending the commercial area that can satisfy the self - correspondence between the image of the restaurant and the image of the commercial area.

Description

BACKGROUND OF THE INVENTION Field of the Invention [0001] The present invention relates to a commercial image generation system,

The present invention relates to a method of generating a commercial image. More particularly, the present invention relates to a system and a method for generating and providing a commercial image by reviewing commercials collected using a big data analysis technique.

The consumer considers various attributes such as restaurant location, food quality, service quality, atmosphere quality, price, past purchasing experience, and brand when making a purchase decision for dining out.

Among these various attributes, the biggest marketing device that attracts consumers in the food service industry is the commercial area, and it is necessary to extract the image of the commercial area and utilize it for marketing.

The main research trends related to brand personality are research on measurement of brand personality and research on the use of measured brand personality (Han Seongho, 2011). Aaker (1997) suggests brand personality measurement items focusing on five factors: sophistication, sincerity, ruggedness, excitement, and competence. (1999), and the results of this study are summarized as follows: (1) The results of this study are as follows: (1) , & Austin, 1999).

Consumers choose brands based on their own evaluations and beliefs about them.

Self-concept is a useful concept for understanding and explaining the structure of customer's choice behaviors (Usakli & Baloglu, 2011). Self-concept refers to 'the overall individual's thoughts and feelings about himself / herself (1987), "The Entanglement of Individuals," Journal of Business Research, Vol.

In addition, it is important to manage the personality of the commercial area because the personality attributes of the commercial area can affect the commercial marketing program. As competition intensifies, there is a growing need to emphasize the unique character of the food service providers that have entered the market. Commercial personality can be used as a differentiator that can be used to build a commercial image with competitive advantage and to bring out the uniqueness of identity. The more the personality of the commercial is the same as the ideal self, the more favorable attitude and the higher the intention to visit.

On the other hand, as the personality of the city is the same as the personality of the city, the intention of future visit is lowered and the favorable attitude is not significantly influenced.

Therefore, the brand image of a foodservice company that wants to shop in a specific business area by measuring the brand image of a commercial area that a consumer perceives about a commercial property (location) Matching is becoming important.

The present invention provides a commercial image generating system and method capable of analyzing big data and providing a commercial image.

Embodiments relate to a communication unit for receiving a commerce image creation request from a user terminal of a specific user via a wired or wireless communication network, and a web crawler and a wrapper-based web crawler ) To collect data, and perform spam filtering of the collected data to generate the commercial image from the non-spam data.

The spam filtering can be calculated by the following equation.

는 클래스 확률로서 (클래스 j에서 유일한 단어의 수 / 전체 클래스에서 유일한 단어의 수) 와 같이 계산하고,

는 특정 단어가 특정 클래스에 속할 확률로서 (단어의 빈도수 + 1)*(클래스 j에의 단어 빈도수 + 전체 클래스에서 유일한 단어 수)와 같이 계산한다. At this time,

(The number of unique words in class j / the number of unique words in all classes) as the class probability,

Is the probability that a particular word belongs to a particular class (frequency of word + 1) * (word frequency in class j + number of unique words in the whole class).

The control unit may perform the web crawler and the wrapper based web crawler to obtain specific data, clean the specific data, and proceed with the spam filtering.

When the commerce recommendation request for a specific restaurant is received from the communication unit, the control unit may match and provide the commodity image having a similarity to the image of the specific restaurant.

And a storage unit for storing the commercial image and the image of the specific restaurant.

In addition, the embodiment includes collecting web data by performing a web crawler and a wrapper-based web crawler in cooperation with a plurality of storage servers, collecting specific data of the collected web data Extracting only the non-spam data by performing spam filtering on the cleaned web data, and generating a commercial image from the non-spam data. to provide.

The present invention can recommend a commercial area that can satisfy the self-consistency between the image of the restaurant and the image of the commercial area by analyzing the big data.

Therefore, it is possible to improve the accuracy and satisfaction of the recommendation to the individual user.

1 is a conceptual diagram showing an entire system including a commercial image generating system of the present invention.
2 is a detailed configuration diagram of a commercial image generating system according to the present invention.
3 is a detailed configuration diagram of the control unit of FIG.
4 is a flowchart showing the operation of the present invention.
5 is a detailed flowchart showing the wrapper-based web crawler steps of FIG.
6 is a graph showing a commercial image of FIG.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily carry out the present invention. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description are omitted, and similar parts are denoted by like reference characters throughout the specification.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between .

Throughout the specification, when an element is referred to as "comprising ", it means that it can include other elements as well, without excluding other elements unless specifically stated otherwise. Also, the terms " part, "" module," and " module ", etc. in the specification mean a unit for processing at least one function or operation and may be implemented by hardware or software or a combination of hardware and software have.

Hereinafter, a commercial image generating system for generating and providing a commercial image and matching with a restaurant image will be described.

Self image and product image are measured by using 5 attributes (Sincerity, Sophistication, Excitement, Competence, Ruggedness) that measure the characteristics of goods and attractions and compare the difference between them. (Self - conformity), and when the difference is large (self - improvement), the satisfaction of the purchased product increases.

Consumers feel the sense of self-definition by consuming a product or service and communicate it to others. Therefore, the brand is preferred to the brand that reflects and expresses the self concept well. The higher the brand identification, the higher the brand loyalty.

Self congruity theory is the theory that consumption behavior focuses on the symbolic function rather than on the consumption object (Sirgy, Johar, Samli, & Claiborne, 1991; Ekinci & Hosany, 2006). In other words, it can be understood that the consumption of goods or brands is a behavioral theory that emphasizes the aspect of strategic behavior that expresses and strengthens the self image of consumers.

Consumers can satisfy their desire for self-consciousness by using products that have an image similar to the self concept they have. It also meets the desire for self-improvement by using products that have an image similar to the ideal self concept that they want to be. Self-consistency may be related to the product as a whole (Sirgy 1982), or to a specific brand image (Fournier 1998).

Consumers have more favorable attitudes and purchase intentions about brands that they perceive to be consistent with their image. In other words, the correspondence between consumer's perceived self-image and brand image has a significant effect on enhancing consumer's choice, purchase, and after-purchase satisfaction, and generally high preference and loyalty I have.

The most representative types presented in self image studies are real self image and ideal self image. Specifically, the actual self refers to the image of the actual oneself, the ideal self, the image of oneself to want to be, the actual social self, the image of oneself that they actually perceive, and the ideal social self, the image that others want to see.

Therefore, when a brand can prove its self in the process of linking the brand - ego and improve its self, it can be selected and selected by the consumer. In other words, the consumer meets self-desire using a brand that proves self and improves self.

The present invention provides a commercial image generation system for selecting a restaurant location that can satisfy consumer expectations based on the theory of self-consistency.

Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a conceptual diagram showing an entire system including a commercial image generating system of the present invention, FIG. 2 is a detailed configuration diagram of a commercial image generating system of the present invention, and FIG. 3 is a detailed configuration diagram of the control unit of FIG.

1 and 2, an overall system including a commercial image generating system according to an exemplary embodiment of the present invention includes a consumer terminal 100, a control server 200, and a storage server 300.

The consumer terminal 100 includes a smart phone, a notebook, a tablet PC, a personal computer, or the like as a terminal through which a user can connect to the control server 200 and request the control server 100 to create a commercial image.

The consumer terminal 100 interacts with the control server 200 through a wired or wireless Internet, and the wireless Internet may be wifi, Bluetooth, or the like.

The consumer terminal 100 can mount an application for the control server 200. [

The plurality of storage servers 300 may be a storage server for storing data of social media such as various websites, blogs, and tweets.

The control server 200 generates a commercial image by receiving a commercial image request transmitted from the user terminal 100 via a wired or wireless Internet. In addition, when matching with a specific restaurant is requested, a commercial having the image that best matches the image of the specific restaurant is selected and sent.

The detailed configuration of the control server 200 is shown in FIG.

Referring to FIG. 2, the control server 200 includes a communication unit 210, a control unit 250, and a storage unit 230.

The communication unit 210 includes a communication module for receiving a signal transmitted by wire or wireless, and may include an antenna, a bandpass filter, and the like when receiving a signal wirelessly.

The communication unit 210 receives a request signal transmitted from the user terminal 100 or transmits a result of the request signal.

The communication unit 210 includes a Wi-Fi module (Wi-Fi Module) that can receive a request signal transmitted from an external user terminal 100 using a Wi-Fi communication network when receiving a signal wirelessly, And a serial communication module such as a USB terminal that is connected by a data cable and can receive a request signal by a serial communication method when receiving a signal.

The control unit 250 is a microprocessor. When receiving a request for a commercial image from the user terminal 100, the control unit 250 receives and analyzes a review of the commercial on various webs to generate an image of the commercial.

If there is a request from the user terminal 100 for a commercial matching with a specific restaurant, the control unit 250 matches and provides a commercial image having high similarity to the profile of the specific restaurant.

3, the control unit 250 includes a web crawler 251, a preprocessing unit 253, a filtering unit 255, an analyzing unit 257, and a matching unit 259.

The web crawler 251 is for collecting data on restaurants from social media such as various websites, blogs, tweeters, etc., and discloses that an application programming interface (API) is provided at social media sites such as Twitter APIs are used to develop crawlers and collect data.

On the other hand, the web crawler can perform additional wrapper-based web crawlers to collect data, since the general site such as blog does not provide API.

The preprocessing unit 253 receives the web data from the web crawler 251 and removes unnecessary characters, symbols, numbers, etc. contained in the received web data.

The filtering unit 255 constructs a training model and classifies the spam document using the training model. Such a classification can filter spam data and extract only non-spam data.

The analyzer 257 receives the non-spam data from the filtering unit 255 and analyzes the image of the corresponding commercial image.

The analysis unit 257 extracts data and converts the data into quantitative data to derive meaningful results. The collected samples are classified into a certain category, and the relationship between the categories is classified into statistical methods .

The matching unit 259 can recommend a commercial commodity to the user by using the 5RRI when the user requests the commodity recommendation matching with a specific restaurant.

The image and the matching result of each of the generated commercial images are stored in the storage unit 230.

Hereinafter, the operation of the commercial image generating system of the present invention will be described with reference to FIGS. 4 to 6. FIG.

FIG. 4 is a flowchart showing the operation of the present invention, FIG. 5 is a detailed flowchart showing a wrapper-based web crawler step in FIG. 4, and FIG. 6 is a graph showing a commercial image in FIG.

Hereinafter, the commercial image generation system is unified into the control server 200.

When a commercial image creation request is received from a specific user via the user terminal 100 to the communication unit 210, the control server 200 starts a commercial image generation operation.

First, the web crawler proceeds to obtain information on the web from various storage servers 300 (S10).

The web crawler stage is for collecting data on restaurants from social media such as various websites, blogs, and tweets. Since social media sites such as Twitter are providing APIs so that they can fetch data, the crawler is developed using API And collects the data.

On the other hand, since a general site such as a blog does not provide an API, a wrapper-based web crawler can be additionally provided to collect data.

In the wrapper-based web crawler stage, the wrapper-based web crawler refers to the structural information for extracting desired information in the collected web page.

In other words, unlike the general web crawler, the wrapper-based web crawler must directly construct the address of the web page including the information to be extracted and use the structure information of the web page to extract desired data.

First, set the URL to visit.

The URL to be visited can be stored by directly storing URLs of various blogs and the like in the storage unit 230.

When the wrapper-based web crawler step is started, several URLs of stored URLs are acquired (S211).

At this time, the acquired URL means URL information to be visited, which is stored in the storage unit 230.

Next, the user visits the corresponding URL and performs the page acquisition step of the corresponding content (S213).

When the page having the content is acquired, the wrapping process is performed in which only the data of the desired content is selected using the structure of each site (S215).

If the selected data is stored and it is determined that all the data is selected in the acquired URL, the wrapper base web crawler is terminated and the crawler step is terminated (S219).

In the case of information that does not provide such an API, a wrapper-based web crawler can be separately performed to acquire information of a greater variety of sites.

Next, the preprocessing step of FIG. 4 is performed (S20).

The preprocessing step is a process of removing unnecessary characters, symbols, numbers and the like included in the acquired web data.

This process is performed in order to reduce errors in data processing in the next step by removing characters expressed as html tag, character / bar, etc. In this step, character of character to be removed is analyzed, and based on the characteristic, Can be defined.

For example, an html tag can be defined as a rule that starts with '<' and ends with '>' as a rule, and can be used to check for string matching if it is a character / If it contains certain characters, you can remove it.

In this preprocessing step, character characteristics to be removed are analyzed, and information is defined by defining rules in the program based on the analyzed contents.

When the preprocessing step ends, the spam filtering step is performed (S30).

The spam filtering step builds a training model and classifies spam documents using training models.

First, after the preprocessing step, unnecessary characters (html tag, symbol, character / vowel, etc.) are removed from the document. For example, in a document that contains content such as "<b> <h1> both price and feature video quality is satisfactory </ h1> </ b> I'm all satisfied. "

Next, tag the part of speech with the part mark tag. In the above example, you can get the tagging results such as "N_price, N_function, N_video, N_quality, Z_not, N_satisfaction", where N is a noun and Z is an adverb .

Next, one training model is created when the part-of-speech specifies the frequency and class value (eg, spam, non-spam) for each word tagged.

The training model according to the above example can be summarized in the following table.

No. Feature Value Frequency The class value One N_price One Non-spam 2 N_ Function One Non-spam 3 N_ Videos One Non-spam 4 N_ picture quality One Non-spam 5 Z_ everyone One Non-spam 6 N_ Satisfaction One Non-spam 7 V_ good One Non-spam ... ... ...

Next, classification is conducted through the training model.

Remove unnecessary characters (html tags, symbols, letters / vowels, etc.) from the document.

For example "<BR> <H3> I really like it. </ H3> </ H3> </ H3> </ H3> </ H3> I like the image quality and the video quality is good. "

Next, tag the part of speech with the part mark tag. Using the results of the previous step, you can get tagging results like "N_ real, V_ good, N_quality, N_video". Where N is a noun, and V is a verb / adjective.

Finally, the extracted feature values are classified using the following equation.

는 클래스 확률로서 (클래스 j에서 유일한 단어의 수 / 전체 클래스에서 유일한 단어의 수) 와 같이 계산한다.

는 특정 단어가 특정 클래스에 속할 확률로서 (단어의 빈도수 + 1)*(클래스 j에의 단어 빈도수 + 전체 클래스에서 유일한 단어 수)와 같이 계산한다. At this time,

Is calculated as the class probability (the number of unique words in class j / the number of unique words in all classes).

Is the probability that a particular word belongs to a particular class (frequency of word + 1) * (word frequency in class j + number of unique words in the whole class).

For example, if the only number of words in a non-spam class is 18, the frequency of a word is 23, and the only number of words in the spam class is 19 and the frequency of words is 24, the likelihood for non-spam and spam is It can be obtained as follows.

likelihood =

P ("N_Real" | Nonspam) * P ("V_Cost" | Nonspam) * P ("N_Image" (1 + 1) / (24 + 37) * (1 + 1) / (24 + 37) ) * (19/37)

= 5.93407E-07

likelihood =

P ("N_Real" | Spam) * P ("V_ Good" | Spam) * P ("N_Image"

(0 + 1) / (23 + 37) * (0 + 1) / (23 + 37) * 18/37)

= 3.75375E-08

Thus, the non-spam probability is 0.941 and the spam likelihood / (spam likelihood + non-spam likeness) = 0.059.

Therefore, the example sentence is classified as non-spam.

Such a classification can filter spam data and extract only non-spam data.

Next, the image of the commercial image is analyzed using the extracted data (S40).

Using 5 Factor, 15 Facet Name, and 42 Traits of Sincerity, Excitement, Competence, Sophistication, and Ruggedness, It refers to a set of human characteristics associated with a brand and represents the source of benefits that provide intangible value to customers.

Commercial image analysis is a method of extracting data and converting it into quantitative data to draw meaningful results. It sets reasonable criteria, classifies the collected samples into a certain category, and deduces the relationship between the classified categories by statistical methods I do it.

The image measurement items are shown in Table 2 below.

Factor Attribute (FacetName) Traits Expanded metrics (E-Traits) refinement
(sophistication)
Upper-class
(upperclass) The upperclass (upperclass) upperclass Goodlooking goodlooking attractive
(charming) Charming charming, magic, magical, sorcerous, witching, wizard, wizardly Smooth smooth, politic, smooth, suave, bland, fluent, fluid, liquid, legato, placid, quiet, still, tranquil, unruffled Sincerity
(sincerity)
beneficial
(wholesome) Wholesome wholesome The original (original) original honest
(honest) Honest honest, honorable, dependable, reliable, true, good, fair Real real, existent, tangible, actual, genuine, literal, substantial, material, veridical The sincere sincere, earnest, solemn Realistic
(down-to-earth) Down-to-earth down-to-earth, earthy Small-town small-town Family-oriented family-oriented cheerful
(cheerful) Cheerful pollyannaish Friendly friendly, well-disposed, welldisposed, audacious Sentimental sentimental, upbeat, bathetic, drippy, hokey, maudlin, mawkish, kitschy, mushy, schmaltzy, schmalzy, sentimental, sappy, soppy, soupy, slushy Strength
(ruggedness)
rough
(tough) Tough rugged, tough, toughened, sturdy, ruffianly, bad, hard, baffling, elusive, knotty, problematic, problematical Rugged furrowed, rugged, broken Outdoors
(outdoorsy) Outdoor (outdoorsy) outdoorsy Western (western) western, westerly Masculine masculine excitement
(excitement)
brand-new
(up-to-date) Up-to-date up-to-date, uptodate, cutting-edge, with-it Contemporary contemporary, modern-day, present-day, contemporaneous Independent, independent, autonomous, self-governing, sovereign, main active
(spirited) Spirited spirited, bouncing, bouncy, peppy, zippy, game, gamy, gamey, gritty, mettlesome, spunky, enlivened Cool (cool) cool, coolheaded, nerveless, allright, fine, okay, hunky-dory Young young, immature, new, youthful, vernal, unseasoned, untested, untried Imaginary
(imaginative) Imaginative imaginative, inventive Unique unique, alone, unequaled, unequal, unparalleled, singular bold
(daring) Daring daring, audacious, venturesome, venturous Trendy trendy, voguish Exciting exciting ability
(competence)
successful
(successful) Successful successful Confident confident, convinced, positive, surefooted, sure-footed Reliable
(reliable) Reliable reliable, dependable, honest, true, authentic Secure secure, unafraid, untroubled, impregnable, inviolable, strong, unassailable, unattackable, dependable, good, safe intellectual
(intelligent) Intelligent intelligent, well-informed, healthy, levelheaded, level-headed, sound, reasoning, thinking The corporate (corporate) corporate, bodied, corporal, embodied, incarnate, collective, incorporated Technical technical, proficient, technological, mechanical, mechanicallyskillful, expert

That is, if there is an expression for an extended measurement item, it is selected as a commercial having one of the five factors.

As an example, trade analysis can be performed as shown in Table 3.

Analysis target Korean Restaurant Restaurant Reviews Collection target Site (Naver Wingbus) that evaluates satisfaction by attributes and overall satisfaction on a multi-point scale Collection period January 3 - January 10, 2011 How to collect Crawling web data with a dedicated web crawler Analysis method Content analysis using pre-processing image data items Sample size Secondary material of Naver Wing spoon, restaurant recommended review 65,535 cases Evaluation period Restaurant recommendations from May 17, 2007 to December 28, 2010

The results of analyzing commercial images of Jongno and Cheongdam-dong with the sample as shown in Table 3 are shown in Fig.

In other words, Jongno and Cheongdam-dong are strong in sophistication, and at the same time, their ability factors are strong, and Jongno is stronger than Cheongdam-dong.

When each image of each image is selected, it is saved.

At this time, when a commercial recommendation request matching the specific restaurant is received from the user terminal 100, the image of the stored commercial image is matched with the image of the specific restaurant (S50).

At this time, 5RRI can be used to recommend a restaurant to a restaurant.

To do this, we can analyze the gap of self-congruence by diagnosing the self-congruence of the restaurant and the specific commercial area, and recommend a commercial area that can achieve self-congruity based on the analysis result.

5RRI can be calculated by the following equation.

In this case, 5RRI (5 Restaurant Recommand Index): commercial image measurement index, II: ideal image, AI: Actual Image: restaurant image, n: And attribute of the commercial image measurement index, respectively.

By analyzing the big data like this, it is possible to recommend the recommended commodity by recommending the commodity similar to the self - correspondence with the restaurant, and to increase the satisfaction.

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.

Claims (6)

delete A communication unit for receiving a commercial image creation request from a user terminal of a specific user via a wired or wireless communication network, and
The method includes collecting data by performing a web crawler and a wrapper-based web crawler in cooperation with a plurality of storage servers, performing spam filtering on the collected data, And a control unit
The spam filtering is calculated by the following equation,

,
At this time,

(The number of unique words in class j / the number of unique words in all classes) as the class probability,

(Frequency of words + 1) * (frequency of words in class j + number of unique words in the entire class) as the probability that a particular word belongs to a certain class. 3. The method of claim 2,
Wherein,
Performing the web crawler and the wrapper based web crawler to obtain the data, and cleaning the data, and then performing the spam filtering. delete 3. The method of claim 2,
And a storage unit for storing the commercial image and the image of the specific restaurant.
delete

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