KR102251513B1 - Method and apparatus for generating contents for learning based on celeb's social media information using machine learning - Google Patents

Abstract

The present invention relates to a learning word list creation method performed by a system for generating a list of learning words in content. The learning word list creation method includes the steps of: receiving an input from a learner, who selects at least one of a list of celebrities provided by the system; accessing social media of the celebrity selected from the celebrity list; selecting any one piece of the content uploaded to the social media; extracting words included in the content by crawling the selected content; generating the learning word list by synthesizing words extracted in an extraction step; and providing the learning word list to the learner. The present invention provides a method for motivating a learner, who wants to learn a foreign language.

Description

Method and device for creating learning contents based on celeb's social media information using machine learning {METHOD AND APPARATUS FOR GENERATING CONTENTS FOR LEARNING BASED ON CELEB'S SOCIAL MEDIA INFORMATION USING MACHINE LEARNING}

The embodiments relate to a method and apparatus for creating a learning container, and more particularly, to a method and system capable of providing a list of learning words extracted from a celeb's social media to a learner.

The number of foreigners trying to learn Hangeul is rapidly increasing thanks to the recent global Hallyu fever. In particular, as the center of Hallyu culture shifts to K-POP, the interest in contents related to Hallyu is explosively increasing in social media and video platforms based on the Internet.

However, the vocabulary for learning Korean was a method of providing the learner with words by consonant order or category, but this had a problem that it was difficult to motivate learners to learn. Foreigners who want to learn Korean have a lot of access to celeb's social media accounts, such as their favorite K-POP artists, and are very interested in the information uploaded on social media, so they learn the words included in the celeb's social media information and learn more efficiently. There is a need to develop a technology that can increase the value.

Korean Patent Registration 　 No. 1737519 [Applicant: Big Luster Co., Ltd.]

The present invention aims to solve the following problems.

An object of the present invention is to provide a method capable of motivating learners to learn a foreign language.

An object of the present invention is to provide a method for a learner to learn words frequently used by celebrities.

The problem to be solved in the present specification is not limited to the above, and may be extended to various items that can be derived by embodiments of the invention described below.

The present invention is a method for generating a list of learning words performed by a system for generating a list of learning words in content, comprising the steps of: (a) receiving an input from a learner selecting at least one of a celeb list provided by the system; (b) accessing the social media of the celebrity selected from the celebrity list; (c) selecting any one of the contents uploaded to the social media; (d) crawling the selected content to extract words included in the content; (e) repeating steps (b) to (d); (f) generating a list of learning words by synthesizing the words extracted in step (e); And (g) providing the learning word list to the learner.

According to an embodiment, further comprising the step of determining the learner's score based on the learner learning the provided learning word list, and the learner's score may be defined in association with the celeb.

According to an embodiment, the word included in the content is a word included in the body of the content, a word included in the comment of the content, a word included in the image of the content, and a voice included in the image of the content. It may include at least one of the extracted words.

According to an embodiment, the step (g) may further include providing an image related to a word included in the learning word list.

According to an embodiment, the step (c) further includes determining an attribute of the content, and in step (d), a word included in the content may be extracted based on the attribute of the content. have.

According to an embodiment, (h) storing a sentence including the word extracted in step (d) in a database; (i) receiving the learner's word input; (j) comparing the word received in step (i) with the database; And (k) providing a sentence including the received word among sentences stored in the database to the learner.

In addition, a computer program according to an aspect of the present invention is for executing a method for generating a list of learning words according to embodiments by being combined with hardware, and may be stored in a computer-readable recording medium.

The method and apparatus for generating words according to the present invention has the effect of giving the learner learning motivation by providing the learner with words used by the learner's favorite celeb on social media.

It should be understood that the effect of the present specification is not limited to the above-described matters, and may be extended to various contents that may be derived from the detailed description of the embodiments of the present invention.

1 is a diagram showing a system according to an embodiment of the present invention.
2 is a diagram showing the configuration of a system according to an embodiment of the present invention.
3 is a flowchart illustrating a method of generating a list of learning words according to an embodiment of the present invention.
4 is a view for explaining a learner input step according to an embodiment of the present invention.
5 is a diagram illustrating a word extraction step according to an embodiment of the present invention.
6 is a diagram illustrating a step of providing a list of learning words according to an embodiment of the present invention.
7 is a flowchart illustrating a method of generating a list of learning words according to an embodiment of the present invention.

In describing an embodiment of the present specification, if it is determined that a detailed description of a known configuration or function may obscure the gist of the embodiment of the present specification, a detailed description thereof will be omitted. In addition, in the drawings, parts not related to the description of the embodiments of the present specification are omitted, and similar reference numerals are attached to similar parts.

In the embodiment of the present specification, when a certain component is "connected", "coupled" or "connected" with another component, it is not only a direct connection relationship, but also an indirect connection in which another component is present in the middle. It may also include a human connection. In addition, when a certain component "includes" or "have" another component, it means that other components may be further included rather than excluding other components unless otherwise stated. .

In the embodiments of the present specification, terms such as first and second are used only for the purpose of distinguishing one component from other components, and unless otherwise noted, the order or importance of the components is not limited. Does not. Accordingly, within the scope of the embodiments of the present specification, the first component in the embodiment may be referred to as a second component in other embodiments, and similarly, the second component in the embodiment is referred to as the first component in other embodiments. It can also be called.

In the exemplary embodiment of the present specification, components that are distinguished from each other are intended to clearly describe each feature, and do not necessarily mean that the components are separated from each other. That is, a plurality of components may be integrated into one hardware or software unit, or one component may be distributed to form a plurality of hardware or software units. Therefore, even if not stated otherwise, such integrated or distributed embodiments are also included in the scope of the embodiments of the present specification.

In the present specification, the network may be a concept including all wired and wireless networks. In this case, the network may mean a communication network in which data exchange between a device, a system, and a device can be performed, and is not limited to a specific network.

The embodiments described herein may have an aspect that is entirely hardware, partially hardware and partially software, or entirely software. In the present specification, "unit", "device", or "system" refers to a computer-related entity such as hardware, a combination of hardware and software, or software. For example, in the present specification, the unit, module, device, or system is a running process, a processor, an object, an executable file, a thread of execution, a program, and/or a computer. It may be (computer), but is not limited thereto. For example, both an application running on a computer and a computer may correspond to a unit, module, device, or system of the present specification.

In addition, in the present specification, the device may be a mobile device such as a smart phone, a tablet PC, a wearable device, and a head mounted display (HMD), as well as a fixed device such as a PC or a home appliance having a display function. In addition, as an example, the device may be an in-vehicle cluster or an Internet of Things (IoT) device. That is, in the present specification, a device may refer to devices capable of an application operation, and is not limited to a specific type. In the following, for convenience of description, a device in which an application is operated is referred to as a device.

In the present specification, the communication method of the network is not limited, and connections between components may not be connected by the same network method. The network may include not only a communication method using a communication network (for example, a mobile communication network, a wired Internet, a wireless Internet, a broadcasting network, a satellite network, etc.) but also a short-range wireless communication between devices. For example, the network may include an object and all communication methods in which the object can network, and is not limited to wired communication, wireless communication, 3G, 4G, 5G, or other methods. For example, wired and/or networks include Local Area Network (LAN), Metropolitan Area Network (MAN), Global System for Mobile Network (GSM), Enhanced Data GSM Environment (EDGE), High Speed Downlink Packet Access (HSDPA), W-CDMA (Wideband Code Division Multiple Access), CDMA (Code Division Multiple Access), TDMA (Time Division Multiple Access), Bluetooth, Zigbee, Wi-Fi, VoIP (Voice over) Internet Protocol), LTE Advanced, IEEE802.16m, WirelessMAN-Advanced, HSPA+, 3GPP Long Term Evolution (LTE), Mobile WiMAX (IEEE 802.16e), UMB (formerly EV-DO Rev. C), Flash-OFDM, iBurst and MBWA (IEEE 802.20) systems, HIPERMAN, Beam-Division Multiple Access (BDMA), Wi-MAX (World Interoperability for Microwave Access), and can refer to a communication network by one or more communication methods selected from the group consisting of ultrasonic communication. However, it is not limited thereto.

Components described in various embodiments do not necessarily mean essential components, and some may be optional components. Accordingly, an embodiment consisting of a subset of the constituent elements described in the embodiment is also included in the scope of the embodiment of the present specification. In addition, embodiments including other elements in addition to the elements described in the various embodiments are included in the scope of the embodiments of the present specification.

Hereinafter, exemplary embodiments of the present specification will be described in detail with reference to the drawings.

1 is a diagram illustrating an example of an operating environment of a system according to an embodiment of the present specification. Referring to FIG. 1, a user device 110 and one or more servers 120, 130, and 140 are connected through a network 1. 1 is an example for explaining the invention, and the number of user devices or servers is not limited as shown in FIG. 1.

The user device 110 may be a fixed terminal implemented as a computer system or a mobile terminal. The user device 110 includes, for example, a smart phone, a mobile phone, a navigation system, a computer, a notebook computer, a digital broadcasting terminal, a personal digital assistant (PDA), a portable multimedia player (PMP), a tablet PC, a game console. console), wearable devices, internet of things (IoT) devices, virtual reality (VR) devices, and augmented reality (AR) devices. As an example, in embodiments, the user device 110 substantially uses one of various physical computer systems capable of communicating with other servers 120-140 through the network 1 using a wireless or wired communication method. It can mean.

Each server may be implemented as a computer device or a plurality of computer devices that communicates with the user device 110 through the network 1 to provide commands, codes, files, contents, services, and the like. For example, the server may be a system that provides each service to the user device 110 accessed through the network 1. As a more specific example, the server may provide a service (for example, information provision) targeted by the application to the user device 110 through an application as a computer program installed and driven on the user device 110. As another example, the server may distribute a file for installing and running the above-described application to the user device 110 and receive user input information to provide a corresponding service.

2 is a block diagram illustrating an internal configuration of a computing device 200 according to an embodiment of the present specification. Such a computing device 200 may be applied to the user device 110 or the server 120-140 described above with reference to FIG. 1, and each device and server are configured by adding or excluding some components, so that the same or similar internal It can have a composition.

Referring to FIG. 2, the computing device 200 may include a memory 210, a processor 220, a communication module 230, and a transceiver 240. The memory 210 is a non-transitory computer-readable recording medium, and is a non-destructive large capacity such as random access memory (RAM), read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. It may include a permanent mass storage device. Here, a non-destructive mass storage device such as a ROM, SSD, flash memory, disk drive, etc. may be included in the above-described device or server as a separate permanent storage device that is separate from the memory 210. In addition, the memory 210 stores an operating system and at least one program code (for example, a browser installed and driven on the user device 110 or a code for an application installed on the user device 110 to provide a specific service). Can be. These software components may be loaded from a computer-readable recording medium separate from the memory 210. Such a separate computer-readable recording medium may include a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, and a memory card.

In another embodiment, software components may be loaded into the memory 210 through the communication module 230 rather than a computer-readable recording medium. For example, at least one program is a computer program installed by files provided by a file distribution system (for example, the above-described server) for distributing the installation files of developers or applications (for example, It may be loaded into the memory 210 based on the above-described application).

The processor 220 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processor 220 by the memory 210 or the communication module 230. For example, the processor 220 may be configured to execute a command received according to a program code stored in a recording device such as the memory 210.

The communication module 230 may provide a function for the user device 110 and the server 120 -140 to communicate with each other through the network 1, and the device 110 and/or the server 120-140 may each This can provide a function for communicating with other electronic devices.

The transmission/reception unit 240 may be a means for interfacing with an external input/output device (not shown). For example, the external input device may include a device such as a keyboard, a mouse, a microphone, and a camera, and the external output device may include a device such as a display, a speaker, and a haptic feedback device.

As another example, the transmission/reception unit 240 may be a means for interfacing with a device in which input and output functions are integrated into one, such as a touch screen.

In addition, in other embodiments, the computing device 200 may include more components than the components of FIG. 2 according to a property of a device to which it is applied. For example, when the computing device 200 is applied to the user device 110, it may be implemented to include at least some of the aforementioned input/output devices, or a transceiver, a global positioning system (GPS) module, a camera, various sensors, Other components such as a database may be further included. As a more specific example, when the user device is a smartphone, various types of accelerometers, gyro sensors, camera modules, various physical buttons, buttons using a touch panel, input/output ports, vibrators for vibration, etc. It may be implemented to further include components.

3 is a flowchart illustrating a method of generating learning content according to an embodiment of the present invention.

3, the learning content generation method includes a learner input receiving step (S310), a social media connection step (S320), a content selection step (S330), a word extraction step (S340), a repetition step (S350), and a list of learning words. It may include a generating step (S360) and a learning word list providing step (S370). Here, the repeating step S350 may be omitted. Here, the learning content may include a learning word, but is not limited thereto, and may include audiovisual information that aids in learning in addition to the learning word (foreign language, etc.).

Each of the above-described steps may be performed irrespective of the listed order, except when they are to be performed in the order listed by a special causal relationship. However, in the following description, for convenience of description, it is assumed that the above-described steps are performed in the order listed.

Hereinafter, each step will be described in detail.

The learner input reception step S310 is a step of receiving an input of a learner selecting at least one of a celeb list provided by the system. The social media access step (S320) is a step of accessing the social media of the selected celeb from the celeb list. The content selection step (S330) is a step of selecting any one of the content uploaded to social media. The word extraction step S340 is a step of extracting words included in the content by crawling the selected content. The repeating step S350 is a step of repeating steps S320 to S340. The learning word list generation step (S360) is a step of generating a learning word list by synthesizing the words extracted in the step (S350). In step S360 of providing a list of learning words, the list of learning words generated in step S350 is provided to a learner.

Hereinafter, a learner input reception step (S310) will be described with reference to FIG. 4.

4 is a view for explaining a learner input step according to an embodiment of the present invention.

Referring to FIG. 4, the system may provide a celeb list to a learner and receive a selection of a learner as a learner's input. In this case, the celebrity list may be provided to the learner in a manner 410 displayed on the display of the user device. Specifically, it is possible to provide a celebrity list on the display of the user device and allow the learner to select it. Alternatively, the system may receive the learner's input by directly inputting the learner's favorite celebrity. In another embodiment, when the celebrity is a singer, a music list is provided to the learner, and the system may receive the singer of the music selected by the learner as an input of the learner.

In step S320 of accessing social media, the system accesses the social media of the celebrity selected by the learner in step S310. Social media is a platform through which information can be shared with others on a network basis, and specifically, may mean an online platform such as Instagram and Facebook. The celebrity selected by the learner in step S310 may have various social media accounts, and the system may access various social media accounts of the celeb.

The content selection step S330 is a step in which the system selects any one of the content uploaded to the social media of the celebrity accessed in step S320. In general, a user can upload content to social media, and the content may include photos, videos, phrases, hashtags, and the like. In addition, other users may provide feedback to the content, such as comments, and information of other users may be included in the content in the form of tags. The system may select at least one of content uploaded by a celebrity selected by the learner to various social media.

The word extraction step S340 is a step in which the system crawls the content selected in step S330 to extract words included in the content. Crawling means extracting the data contained in the content. Specifically, the system may extract a word from data included in the content selected in step S330. For example, the system may extract at least one of a word included in the body of the content, a word included in the comment of the content, a word included in the image of the content, and a word extracted from the voice included in the image of the content. . For example, words included in an image of the content may be extracted using an optical character recognition (OCR) method. Here, the word may include not only a word expressed as a single word, but also a combination of a plurality of words, or may mean a sentence including a word. Specifically, if a celebrity uploads the phrase'always happy' on their social media, the system can extract not only'always' and'happiness' as words, but also the entire'always happy' as words. have.

For example, the system may extract emotion information based on words or sentences included in the selected content. After that, the system can manage the extracted emotion information as tag information by tagging the selected content. For example, a specific emotion (eg, joy, sadness, etc.) may be extracted by analyzing words included in content uploaded by a specific celebrity. After that, words representing emotions (joy, sadness, etc.) extracted in the corresponding content can be registered as tag information. Accordingly, if the system provides to learn the word'joy', the content may be provided to the user.

As another example, the system may extract subject information based on words or sentences included in the selected content. After that, the system can manage the extracted subject information as tag information by tagging the selected content. For example, a specific topic (eg, bank visit, birthday party, etc.) can be extracted by analyzing words included in content uploaded by a specific celebrity. After that, words representing the extracted topic (bank, birthday, congratulation, etc.) can be registered as tag information. Accordingly, if the system provides to learn the word'birthday', the content may be provided to the user.

Hereinafter, the word extraction step S340 will be described in more detail with reference to FIG. 5.

5 is a diagram illustrating a word extraction step according to an embodiment of the present invention.

Referring to FIG. 5, the system may access (S320) any one of various social media 510 of a celeb selected by the learner in step S310. In this case, the system may select any one of the contents uploaded to the social media 510 (S330), and extract a word by crawling the selected content (S340).

In this case, the uploaded content may include an image, text of the body, a comment, and the like, and the system may extract at least one of them. Specifically, the system may extract the word 520 of'selfie' from the comments of the content. Alternatively, the word'advertisement' may be extracted from the hashtags included in the text. Alternatively, when an image is uploaded to the content, a word may be extracted from a voice included in the image.

Although not shown in the drawing, the step S330 further includes determining the attribute of the content, and in step S340, a word included in the content may be extracted based on the attribute of the content. In one embodiment, the attribute of the content may mean the type of the content. Specifically, it is possible to determine whether the content is a music video, V-log, an interview video, or an image. The system analyzes the data included in the content, hashtags, titles, comments, etc. Can be judged.

In this case, the system may extract words included in the content in different ways according to the properties of the content. In an embodiment, when the content is an interview video or a vlog, a word may be extracted from a voice included in the video. On the other hand, when the content is a music video, words may not be extracted from lyrics included in the music video.

The repeating step S350 is a step of repeating steps S320 to S340. In one embodiment, the system may extract words by repeatedly selecting content uploaded to various social media of a celeb selected by the learner. Specifically, the first word may be extracted from the first content uploaded to the celeb's Facebook, and the second word may be extracted from the second content uploaded to the celeb's Instagram. Alternatively, the third word may be extracted from the third content uploaded to the celeb's Facebook, and the fourth word may be extracted from the fourth content uploaded to the celeb’s Facebook.

The learning word list generation step (S360) is a step of generating a learning word list by synthesizing the words extracted in the step (S350). Specifically, as in the above-described embodiment, when the first word, the second word, the third word, and the fourth word are extracted through the repetition step S350, a list of learning words may be generated by combining them. More specifically, the learning word list may include all of the first word, the second word, the third word, and the fourth word, or at least one of them.

As another example, in the step of creating a learning word list, specific audio-visual information may be matched with words and listed. For example, the word'birthday' means a candle or a photo of content uploaded on a birthday.

In addition, when listing the learning words, the system may determine the listing order of the learning words based on the importance of the learning words. Here, the importance of the learning word may be determined according to the location or frequency in the content from which the learning word is extracted. For example, whether the word is a word in the body of the content on social media, a word in a comment, how often it appears, and whether it is a large comment replied by a content uploader (celebrity). In addition, when a second user comments on the content uploaded by the celebrity and the celebrity comment on the comment, the second user’s comment and the celeb’s large comment may be listed in pairs to form one learning word. .

In step S370 of providing a list of learning words, the system provides the list of learning words generated in step S350 to a learner. Specifically, the list of learning words may be provided to learners in a manner displayed on the display of the user device. The method for generating a learning word list may further include determining a score of the learner based on the learner learning the provided learning word list. In an embodiment, when a learner learns a list of learning words provided to a user through steps S320 to S360, the system may determine the learner's score and provide it to the learner. Here, the learner's score provided to the learner may be defined in association with a celebrity. In a specific embodiment, when the celebrity selected by the learner in step S310 is'J-Hope', the learner's score may be expressed in the form of J-Hope 10, J-Hope 200, or the like.

Hereinafter, the step S370 of providing a list of learning words will be described in more detail with reference to FIG. 6.

6 is a diagram illustrating a step of providing a list of learning words according to an embodiment of the present invention.

Referring to FIG. 6, a list of learning words 620 included in content may be provided to a learner. In this case, the step of providing a learning word list (S370) may further include providing an image related to a word included in the learning word list. Specifically, referring to FIG. 6, when the word'Monday' is provided, an image 630 related to Monday may be provided to the learner together. For example, an image related to Monday may be selected and provided as an image including a celebrity selected by the learner. For example, if there is a photo taken by a celebrity in front of the calendar, such a photo may be selected as an image for learning. That is, not only the object (calendar or Monday) in the image, but also information about the person (whether you are a celebrity) may be used together. As another example, the learner's score 610 may be provided to the user along with a list of learning words.

In one example, the system may cluster words with high grit among words included in one content to generate and list learning words in word cluster units. For example, words with high relevance among words included in content uploaded to social media may be included in one cluster using Word2vec. Referring to FIG. 5, words related to selfies are exposed, such as'selfie' twice,'ulzzang' once,'face' once, and'handsome' once. In this case, the system can provide the learners with words such as selfies, ulzzang, handsomeness, and faces as one learning word cluster.

On the other hand, there may be cases where images are subject to copyright protection. The system checks whether or not the selected learning image is subject to copyright protection, and if it is subject to copyright protection, the image may be changed. For example, the system may search and provide a copyright-free image similar to the corresponding image, or the system may provide the corresponding image by lowering the resolution.

7 is a flowchart illustrating a method of generating a list of learning words according to an embodiment of the present invention.

Referring to FIG. 7, the method of generating a learning word list may further include a database storage step S710, a word input reception step S720, a database comparison step S730, and a sentence provision step S740.

The database storage step S710 is a step of storing a sentence including the word extracted in step S340 in a database. Specifically, when the celebrity uploads the phrase'always be happy and be healthy' on social media, the words extracted in step S340 may be'always','happiness','health', and the like. Alternatively, as described above, it may be a combination of words such as'always happy' and'be healthy'. Or, it could be the entire sentence'Always be happy, be healthy'. At this time, the system may store the extracted word in the database.

The word input reception step S720 is a step in which the system receives the learner's word input. Learners can enter words in a variety of ways. Specifically, the learner may directly input a word and allow the system to receive it, and select any one of the word list provided by the system and allow the system to receive it.

In the database comparison step (S730), the system compares the word received in the step (S720) with the database. Specifically, when the learner inputs the word'happiness' in step S720, the system may search the database for the word or sentence uploaded by the celebrity selected by the learner including the word'happiness'. That is, the database comparison step refers to a process of extracting a word that is the same as or corresponds to an input word from among words in the database.

The sentence providing step (S740) is a step of providing a sentence including a received word among sentences stored in the database to a learner. In one embodiment, this is a step in which the system provides the sentence searched for in step S730 to the learner. Specifically, specifically, when the learner inputs the word'happiness' in step S720, the system writes sentences such as'always happy' and'always happy, be healthy' including'happiness' in the database. It can be searched and provided to learners.

As another example, the system determines the properties of the content uploaded by the celeb (whether it is a music video, a daily vlog, an interview, etc.) and determines the content according to the properties of the content through hashtag analysis, video analysis, title analysis, etc. You can decide whether to import the spoken sentence from your video as a learning word. For example, when the property of the content is a music video, text may not be retrieved from the content, and in the case of an interview or daily vlog, the text may be extracted from the video. In general, since song lyrics may contain words that are not used in everyday life, the above features can be used to organize learning words based on words that are frequently used as much as possible.

As another example, the system can machine learn the language habits of a specific celeb by converting interview images, vlogs, and uploaded content articles into big data. Here, the language habit may be a combination of intonation and specific words. When a specific word is input to the learning model generated and learned in this way, a voice output for uttering a specific word may be generated, or a sentence including a specific word may be generated and provided to the learner. A word or sentence output in this way may be applied to a language habit such as speech intonation of a specific celebrity or repetition of a specific word.

For example, if the word "always happy" is input, the learning model can output the voice of "always happy" by applying the characteristic when the celebrities utter words including "always" or "happiness". Or, if you enter the word "always happy" and the celebrity frequently expresses "always happy and healthy" or a similar form (always happy and has a healthy year, etc.), the learning model will be "always happy and healthy." You can also output the voice or text of ".

According to the above, if a celebrity you like uploads content on social media, there is an advantage that learning about words included in the content can be automatically provided. In addition, through deep learning, there is an advantage that other words and sentences can be provided with learning words in the form of the celeb's language habits applied.

The embodiments described above may be implemented at least partially as a computer program and recorded on a computer-readable recording medium. The recording medium in which a program for implementing the embodiments is recorded and the computer-readable recording medium includes all kinds of recording devices in which data that can be read by a computer is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, and optical data storage devices. In addition, the computer-readable recording medium may be distributed over a computer system connected through a network, and computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing this embodiment may be easily understood by those of ordinary skill in the art to which this embodiment belongs.

The present specification described above has been described with reference to the embodiments shown in the drawings, but these are only exemplary, and those of ordinary skill in the art will understand that various modifications and variations of the embodiments are possible therefrom. However, such modifications should be considered to be within the scope of the technical protection of the present specification. Accordingly, the true technical protection scope of the present specification should be determined to include other implementations, other embodiments, and equivalents to the claims according to the technical spirit of the appended claims.

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Claims (7)

A method of generating a list of learning words performed by the system for creating a list of learning words in the contents
Receiving an input of a learner selecting at least one of a celeb list provided by the system;
Accessing a social media of a selected celeb from the celeb list;
Selecting any one of the contents uploaded to the social media;
Extracting words included in the content by crawling the selected content;
Generating a list of learning words by synthesizing the words extracted in the extracting step; And
Including the step of providing the learning word list to the learner,
The step of generating the learning word list,
The order of listing the learning words is determined based on the importance of the learning words, where the importance of the learning words is determined according to the location and frequency in the content from which the learning words are extracted,
The location in the content includes the body of the content, a comment, and a large comment,
Learning that the first comment and the first large comment form one learning word as a pair when another user puts a first comment on the content uploaded by a celebrity and the celebrity puts a first large comment on the first comment How to create a word list.

The method of claim 1,
Further comprising the step of determining the score of the learner based on the learner learning the provided learning word list,
The learner's score is defined in association with the celebrity.
How to create a list of learning words.
The method of claim 1,
Words included in the above content,
Including at least one of a word included in the body of the content, a word included in the comment of the content, a word included in the image of the content, and a word extracted from the voice included in the image of the content
How to create a list of learning words.
The method of claim 1,
The providing step,
Further comprising the step of providing an image related to the words included in the learning word list
How to create a list of learning words.
The method of claim 1,
The selecting step,
Further comprising the step of determining the property of the content,
In the extracting step,
Extracting a word included in the content based on the attribute of the content,
How to create a list of learning words.
The method of claim 1,
Storing a sentence including the word extracted in the extracting step in a database;
Receiving the learner's word input;
Comparing the word received in the step of receiving the word input with the database; And
Providing a sentence including the received word among sentences stored in the database to the learner
How to create a list of learning words.
A computer program stored in a computer-readable medium to execute the method for generating a list of learning words according to any one of claims 1 to 6 in combination with hardware.

Images