KR20220074574A - A method and an apparatus for analyzing real-time chat content of live stream - Google Patents

Abstract

A service providing apparatus according to an embodiment of the present invention includes: a collecting unit for collecting user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information; a processing unit for processing the information collected by the collection unit into structured data and unstructured data for marketing analysis; an analysis unit for learning and analyzing the processed structured data or unstructured data; and transferring the real-time chat text associated with the live stream received from the user interface unit to the analysis unit, and obtains response information obtained from the analysis unit in response to the real-time chat text associated with the live stream, and visualizes the content through the user interface unit It includes; a real-time chat analysis service providing unit for outputting.

Description

Knowledge graph-based live stream real-time chat content analysis method and device {A METHOD AND AN APPARATUS FOR ANALYZING REAL-TIME CHAT CONTENT OF LIVE STREAM}

The present invention relates to a service providing method and an apparatus therefor. More specifically, the present invention relates to a knowledge graph-based live stream real-time chatting content analysis method and an apparatus therefor.

As Internet technology develops, online services are being provided in various fields such as education, shopping, entertainment, and marketing. Among them, online marketing first started as a form of unilaterally delivering information about a company or product to users through a homepage.

In particular, with the advent of high-performance terminal devices such as smart phones and the construction of inexpensive wireless networks such as Wi-Fi networks, the number of users in the online environment is increasing rapidly, and various information collected in such an online environment is As it is used as data, it is also used for marketing and effectiveness analysis.

However, due to the recent rapid increase in the amount of analysis data, the cost and time for analysis thereof are also rapidly increasing. In particular, the marketing environment due to the increase in the amount of data requires a production infrastructure that requires additional hiring of analysts and marketers to analyze marketing cost-effectiveness, etc., and as a result, small and medium-sized enterprises that are difficult to hire are fragmented and intuitive. It is very difficult to receive effective marketing analysis consulting at low cost as only some analysis is possible.

Accordingly, at present, decision-making marketing that relies on the intuition and sense of marketer employees is mainly used in small and medium-sized enterprises (SMEs), and despite the increase in the amount of data that can be analyzed, it is difficult to follow the flow. .

Accordingly, there is a need for an efficient marketing analysis service that facilitates marketing decision-making based on accurate grounds even at low cost.

The present invention has been devised to solve the above problems, and by effectively providing a marketing information analysis service based on a marketing specialized knowledge graph model based on analysis information for each marketing channel through artificial intelligence technology, marketing with low cost and high efficiency An object of the present invention is to provide a method and apparatus for providing an efficient marketing analysis service that can support decision-making.

In addition, in order to effectively provide marketing decision-making and analysis results as described above, the present invention can provide optimized feedback and marketing information services to users through content analysis shared with a real-time chat function provided on social media. An object of the present invention is to provide a knowledge graph-based live stream real-time chatting content analysis method and apparatus.

A service providing apparatus according to an embodiment of the present invention for solving the above-described problems, provides user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information. a collecting unit to collect; a processing unit for processing the information collected by the collection unit into structured data and unstructured data for marketing analysis; an analysis unit for learning and analyzing the processed structured data or unstructured data; and transferring the real-time chat text associated with the live stream received from the user interface unit to the analysis unit, and obtains response information obtained from the analysis unit in response to the real-time chat text associated with the live stream, and visualizes the content through the user interface unit It includes; a real-time chat analysis service providing unit for outputting.

In a service providing method according to an embodiment of the present invention for solving the above problems, user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information are provided. collecting; processing the information collected in the step of collecting the knowledge base information into structured data and unstructured data for marketing analysis; an analysis step of learning and analyzing the processed structured data or unstructured data; and transferring the real-time chat text associated with the live stream received from the user interface unit to the analysis unit, and obtains response information obtained from the analysis unit in response to the real-time chat text associated with the live stream, and visualizes the content through the user interface unit Includes a real-time chat analysis service providing step of outputting.

According to an embodiment of the present invention, by effectively providing a marketing information analysis service based on a marketing specialized knowledge graph model based on analysis information for each marketing channel through artificial intelligence technology, efficient marketing that can support marketing decision making at low cost and high efficiency It is possible to provide a method and an apparatus for providing an analysis service.

In addition, according to an embodiment of the present invention, in order to effectively provide marketing decision-making and analysis results, a real-time knowledge graph-based live stream capable of providing visualized content and marketing information service using a real-time chat function of social media A chatting content analysis method and apparatus may be provided.

In addition, a positive marketing effect can be achieved by providing immediate feedback through a function that enables real-time chatting between operators and users using social media. Real-time chat information generated in social media can be analyzed to understand the intention and emotion of the user, thereby providing content or marketing information that can be optimized to the user. And it can be considered as a way to overcome the limitations on manpower employment and cost, which can be limited by the marketing environment, and the feedback provision method through this real-time chat information analysis can be provided so that it can be utilized in a one-person media environment. do.

1 is a block diagram schematically showing an entire system according to an embodiment of the present invention.
2 is a block diagram illustrating in more detail an apparatus for providing a marketing service according to an embodiment of the present invention.
3 is a flowchart illustrating an operation of an apparatus for providing a marketing service according to an embodiment of the present invention.
4 is a block diagram for explaining in more detail a knowledge graph building module according to an embodiment of the present invention.
5 is a flowchart illustrating an operation of a knowledge graph building module according to an embodiment of the present invention.
6 is a relationship diagram for explaining a knowledge graph construction and semantic mapping process according to an embodiment of the present invention.
7 is a block diagram for explaining in more detail a real-time chat analysis service providing unit of the service providing unit according to an embodiment of the present invention, and FIG. 8 is a flowchart for explaining a service providing process according to an embodiment of the present invention.

The following is merely illustrative of the principles of the invention. Therefore, those skilled in the art will be able to devise various devices that, although not explicitly described or shown herein, embody the principles of the present invention and are included within the spirit and scope of the present invention. Further, it is to be understood that all conditional terms and examples listed herein are, in principle, expressly intended solely for the purpose of enabling the concept of the present invention to be understood, and not limited to the specifically enumerated embodiments and states as such. should be

Moreover, it is to be understood that all detailed description reciting the principles, aspects, and embodiments of the invention, as well as specific embodiments, are intended to cover structural and functional equivalents of such matters. It should also be understood that such equivalents include not only currently known equivalents, but also equivalents developed in the future, i.e., all devices invented to perform the same function, regardless of structure.

Thus, for example, the block diagrams herein are to be understood as representing conceptual views of illustrative circuitry embodying the principles of the invention. Similarly, all flowcharts, state transition diagrams, pseudo code, etc. may be tangibly embodied on computer-readable media and be understood to represent various processes performed by a computer or processor, whether or not a computer or processor is explicitly shown. should be

The functions of the various elements shown in the drawings including a processor or functional blocks represented by similar concepts may be provided by the use of dedicated hardware as well as hardware having the ability to execute software in association with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or a plurality of separate processors, some of which may be shared.

In addition, the clear use of terms presented as processor, control, or similar concepts should not be construed as exclusively referring to hardware having the ability to execute software, and without limitation, digital signal processor (DSP) hardware, ROM for storing software. It should be understood to implicitly include (ROM), RAM (RAM) and non-volatile memory. Other common hardware may also be included.

In the claims of the present specification, a component expressed as a means for performing the function described in the detailed description includes, for example, a combination of circuit elements that perform the function or software in any form including firmware/microcode, etc. It is intended to include all methods of performing the functions of the device, coupled with suitable circuitry for executing the software to perform the functions. Since the present invention defined by these claims is combined with the functions provided by the various enumerated means and in a manner required by the claims, any means capable of providing the functions are equivalent to those contemplated from the present specification. should be understood as

The above objects, features and advantages will become more apparent through the following detailed description in relation to the accompanying drawings, and accordingly, those of ordinary skill in the art to which the present invention pertains can easily implement the technical idea of the present invention. There will be. In addition, in the description of the present invention, if it is determined that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

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

1 is a conceptual diagram schematically illustrating an entire system according to an embodiment of the present invention.

The entire system according to an embodiment of the present invention includes a marketing information analysis service providing apparatus 100 , a marketing platform 200 and one or more user terminals 300 connected through one or more mutually distinct channels, and a marketing information analysis service The providing apparatus 100 may be connected to the machine learning module 400 or may include the machine learning module 400 .

The marketing information analysis service providing apparatus 100 may be connected to each platform 200 and the user terminal 300 through a wired/wireless network to provide a marketing information analysis service, and may analyze marketing information based on learning and artificial intelligence For this purpose, it may be connected to the machine learning module 400 or include the machine learning module 400, and devices or terminals connected to each network may communicate with each other through a preset network channel.

Here, each of the networks is a local area network (LAN), a wide area network (WAN), a value added network (VAN), a personal local area network (PAN), a mobile communication network ( It can be implemented in all types of wired/wireless networks such as mobile radiocommunication network) or satellite communication networks.

The user terminal 300 may include various server devices, network devices, or terminal devices that access the marketing information analysis service providing apparatus 100 for the purpose of receiving a marketing analysis service for marketing decision making.

In particular, in general, the user terminals 300 may be connected to the marketing information analysis service providing apparatus 100 through an individual security network, and the marketing information analysis service providing apparatus 100 may be connected to each user terminal 300 through each security network. ) may collect channel-based marketing document data based on the company information provided from them, and provide analysis result information corresponding to the collected document data to the user terminal 300 through the security network. Here, the security network may be an encryption network, and the service-registered user terminal 300 pre-stores decryption key information according to corporate authentication, and stores the marketing analysis result information received from the marketing information analysis service providing apparatus 100, Decryption according to the decryption key information can be obtained and output.

According to this embodiment of the present invention, the user terminals 300 may have completed the basic information registration process corresponding to the marketing information analysis service providing apparatus 100 .

For example, the user terminal 300 may be a terminal that is provided with a marketing information analysis service as a member of each company. For example, it may be a terminal of a company that performs direct marketing decision-making, a terminal of a company that provides marketing services in partnership with a plurality of companies, or a terminal of a network service company that mediates data between a plurality of networks.

In addition, the marketing information analysis service providing apparatus 100 receives company information from each user terminal 300 , collects marketing document data based on a marketing network channel classified in advance based on the received company information, and the document data By learning and processing the unstructured data according to the processing of the machine learning module 400, using the knowledge graph information and ontology information built in advance, the learning information and the structured data collected and analyzed in advance, a marketing-specialized knowledge graph model create

Accordingly, the marketing information analysis service providing apparatus 100 may process a marketing market trend and demand prediction analysis using the marketing specialized knowledge graph model, and transmit marketing analysis information according to the processed result information to the user terminal 300 . can provide

Here, the marketing-specialized knowledge graph may be constructed by semantic mapping processing of pre-established knowledge graph model information according to a natural language analysis result of company information and marketing document collection information, and the marketing information analysis service providing apparatus 100 ) can collect, store, and manage dictionary (DICTIONARY) information required for natural language processing and text analysis for such semantic mapping processing and ontology information for constructing a classification system in advance.

More specifically, the marketing information analysis service providing apparatus 100 collects and sets a dictionary and a classification system specialized for marketing in advance, and natural language analysis-based learning of marketing document information collected for each marketing channel in response to corporate information According to the processing, semantic mapping may be performed on the pre-established knowledge graph. Accordingly, the meaning-mapped marketing-specialized knowledge graph is specialized for marketing and includes the latest information and rich synonym information, and can include rich context (CONTEXT) and relationship (ASSOCIATION) information.

Such a marketing-specialized knowledge graph can include relationship information between keywords, can be used for various solutions such as marketing trend analysis and future predictive analysis, and can also be used to individually create a subdivided dictionary and classification system for each marketing field. can

The marketing specialized knowledge graph according to an embodiment of the present invention is a graph-based data model including relationship information between knowledge keywords by setting keyword information, which is a marketing entity, as a node, and representing the relationship between each node as an edge. may be exemplified by a relational data model, but the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention is based on the recently proposed SEMANTIC WEB technology to overcome the complexity and performance limitations of the relational data model. Based on this, it is possible to create higher efficiencies, expand the knowledge expression method, and solve the problems of scalability of data models and interoperability between systems.

In particular, the dictionary and classification system for text analysis is manually created by experts in a specific field, and as described above, there is a problem of cost increase due to the increase in the amount of data. There is a problem that this falls, and the technology itself, such as a conventional web ontology language (OWL, Ontology Web Language), has problems with low model complexity and reusability.

Accordingly, the apparatus 100 for providing marketing information analysis service according to an embodiment of the present invention provides learning information learned through machine learning from unstructured data analysis information and knowledge extracted from structured data in order to construct a marketing-specialized knowledge graph. Based on the graph transformation rule information, the pre-established knowledge graph information obtained from the open knowledge graph, and the ontology rule information obtained from the pre-managed relational database, the diversified marketing knowledge data is efficiently semantically mapped, enabling automation while Its accuracy and performance can be improved.

In particular, the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention may provide keyword classification and system information based on a marketing specialized knowledge graph through the semantic mapping processing of the diversified marketing knowledge data, It makes it easy to reflect recent issues keywords or new words for marketing purposes, and it is possible to quickly build and process language compatibility information for marketing purposes (eg, foreign language data corresponding to Korean transliteration).

Meanwhile, the platform 200 may be a marketing target network platform, and may be connected to the marketing information analysis service providing apparatus 100 through each access channel. Each channel may be, for example, site address information corresponding to a specific platform, the marketing information analysis service providing apparatus 100 collects marketing document data for each platform channel determined in response to the site address information, and the collected results can be stored and analyzed. Additionally, the platform 200 may be configured with various types of platforms that can provide social media services.

In addition, the machine learning module 400 used in the analysis may process parallel analysis of structured and unstructured data, and hybrid-type document classification processing for this may be performed in advance. Hybrid-type document classification processing is marketing document data using a machine learning-based primary document classification process and secondary classification information using an ontology dictionary and a linguistic rule from classification information obtained from the primary document classification process It may include a secondary classification process for classifying As such, classification information according to the primary and secondary classification may be used as re-learning training information of the machine learning module 400 .

And, according to the classification and analysis processing, the marketing information analysis service providing apparatus 100 according to an embodiment of the present invention may provide an analysis information service for effective marketing to the user terminal 300 . For example, the marketing information analysis service providing apparatus 100 may provide a keyword dictionary construction service for market trend analysis, a digital influence quantification service for each keyword, a trend prediction information providing service according to a prediction model, etc. to the user terminal 300 . can

In addition, in order to provide such a service, the marketing information analysis service providing apparatus 100 analyzes text or voice-based request data received from the user terminal 300 and provides a marketing analysis information providing service using an artificial intelligence chatbot function. You may.

2 is a block diagram illustrating in more detail an apparatus for providing a marketing service according to an embodiment of the present invention.

Referring to FIG. 2 according to an embodiment of the present invention, the apparatus 100 for providing a marketing information analysis service according to an embodiment of the present invention includes a control unit 110 , a communication unit 120 , and a user management unit 130 . ), a channel-based information collection unit 140 , an analysis data processing unit 150 , a dashboard configuration unit 160 , a service providing unit 170 , and a storage unit 190 .

The control unit 110 generally controls the execution of the operation and function of each component, including the marketing document data information collection, analysis data processing, dashboard configuration, and marketing information analysis service provision of the marketing information analysis service providing device 100 . do. For example, the control unit 110 may be implemented as a processor for controlling all or part of a function of providing an analysis result of information collected from the platforms 200 to the user terminals 300 or a program for executing the same. can

In addition, the communication unit 120 is a network between the marketing information analysis service providing apparatus 100 and a wireless communication system including a mobile communication network or Internet network, or between the service providing apparatus 100 and the platform 200 or the user terminal 300 is located. It may include one or more communication modules that enable wired/wireless communication therebetween. For example, the communication unit 120 may include a modem that encodes and modulates a transmitted signal and demodulates and decodes a received signal, or an RF front end that processes an RF signal.

Meanwhile, the user management unit 130 performs user registration and account management for one or more user terminals 300 using the service providing apparatus 100 .

The user management unit 130 receives authentication information including at least one of account identification information and terminal identification information of a person in charge of a logged-in company or a marketing service provider from the user terminal 300, and uses the authentication information to store user information Registration can be processed. Accordingly, the user management unit 130 may register and manage information on the platform 200 to provide or analyze a marketing service and information on the user terminal 300 corresponding thereto for each marketing channel.

In addition, the channel-based information collection unit 140 collects marketing document data from the platform 200 corresponding to the user terminals 300 managed by the user management unit 130 through each connected data channel, and for each channel The collected marketing document data is output to the analysis data processing unit 150 .

Here, the marketing document data may form basic analysis information processed by the analysis data processing unit 150 according to an embodiment of the present invention. The marketing document data may include, for example, web page document data collected from the platform 200 for each channel, keyword data collected in response to a preset format, or site source code information.

For such channel-specific collection, the channel-based information collection unit 140 stores a keyword crawler that collects and stores keywords classified by industry/subject/brand in response to each platform 200, a user request collection process, and A collection process manager that allocates a collection process for each channel, a collector for each channel that accesses the platform 200, performs collection by channel and stores the collection result in the storage unit 190, and a problem that collection is stopped due to site source change It may include a collection site source management manager that prepares for and periodically compares and reports newly updated information.

Accordingly, the channel-based information collection unit 140 may access the platform 200 through a specific channel according to channel information requested from the user terminal 300 or preset in response to the user terminal 300 . In addition, the channel-based information collection unit 140 receives the marketing document data to be collected according to keyword information received from the user terminal 300 or preset corresponding to the user terminal 300 through a data channel connected to the platform 200 . It can be collected through the Star Collector.

In addition, the channel-specific collector of the channel-based information collection unit 140 may store the collected marketing document data in the collection result database of the storage unit 190 .

For example, the channel-based information collection unit 140 identifies the channel information of the platforms 200 for each industry/subject/brand corresponding to the classification information requested from the user terminal 300, and through the channel, the user terminal ( 300) may determine a suitable collection site, and may collect and store marketing document data corresponding to preset keyword information from the determined site. In particular, the preset keyword information may be obtained from a marketing ontology-based knowledge graph processed by the analysis data processing unit 150 , which will be described in more detail later.

In addition, the channel-based information collection unit 140 may register site information of the platform 200 on which marketing document data is collected, and monitor it periodically, and when source code update information is generated, it is sent to the user terminal 300 . It provides an alarm and can collect and store updated data.

In addition, the analysis data processing unit 150 may perform document classification processing of the marketing document data collected by the channel-based information collection unit 140 , and may generate or build a marketing-specialized knowledge graph model using the classified document data.

According to an embodiment of the present invention, the marketing specialized knowledge graph model includes pre-built keyword-based knowledge graph information, pre-collected ontology information, machine learning learning information of the collected and classified document data, and structured data information. can be built using The marketing-specialized knowledge graph model may be a modular ontology model data, and the ontology model data includes a core ontology built from key concepts, relationship information, daily keywords and emotional keyword information, and real-time machine learning-based document classification to reflect the latest keywords. It can be designed to be layered with domain ontology built from the data obtained, and interoperability can be secured by the semantic web standard technology. The semantic web standard technology may include, for example, a conversion processing technology into a standard protocol language corresponding to an ontology description query, and the converted ontology description query format is RDF (Resource Description Framework) format, OWL (Web Ontology language) Format, Sparkle (SPARQL, Protocol and RDF Query Language) format, etc. may be exemplified.

For this processing, the analysis data processing unit 150 includes a knowledge graph construction module 151 that processes knowledge graph construction, a dictionary construction module 152 corresponding to the domain ontology, and filtering classification of structured and unstructured documents It may include a document classification module 153 for each. Accordingly, the analysis data processing unit 150 may provide various service information based on marketing ontology by using the generated or constructed marketing specialized knowledge graph model. In particular, the knowledge graph construction module 151 may acquire machine learning-based marketing learning information, and the acquired marketing learning information may be used to build a marketing-specific knowledge graph model.

In order to provide the marketing analysis information service, the dashboard configuration unit 160 may configure a marketing analysis dashboard interface to be provided to the user terminal 300 , and the dashboard may be in the form of a GUI (GRAPHIC USER INTERFACE) such as a web interface. may be visually or aurally output through the user terminal 300 .

In addition, the dashboard configuration unit 160 may configure an artificial intelligence chatbot-based marketing interface dashboard for a user-friendly marketing information analysis service, and through this marketing interface dashboard, the user terminal 300 requests It can provide various services such as market trend analysis, demand forecast analysis, keyword influence analysis, new word keyword dictionary, and product competitiveness analysis.

Then, the service providing unit 170 receives the service request of the user terminal 300, and through the dashboard interface configured in the dashboard configuration unit 160, the marketing information analysis service result corresponding to the service request, the user terminal ( 300), and may include a service manager provided by it.

In addition, the service providing unit 170 may receive the real-time chat text of the user terminal 300 , visualize the result information obtained through analysis based on the received chat text, and deliver it to the dashboard configuration unit 160 .

On the other hand, the storage unit 190 includes one or more storage media for storing program information for the operation of the above-described control unit 110 and the operation of the above-described components, and may include one or more databases according to each purpose. have.

3 is a flowchart illustrating an operation of an apparatus for providing a marketing service according to an embodiment of the present invention.

Referring to FIG. 3 , the apparatus 100 for providing a marketing information analysis service according to an embodiment of the present invention first collects platform channel-based marketing document data according to a service request from the user terminal 300 ( S101 ).

Then, the marketing information analysis service providing apparatus 100 performs hybrid document classification processing according to primary filtering of marketing document data and secondary filtering based on machine learning (S105).

Then, the marketing information analysis service providing apparatus 100 extracts unstructured data from the marketing document data (S105), and obtains machine learning-based marketing learning information corresponding to the unstructured data (S107).

Thereafter, the marketing information analysis service providing apparatus 100 generates a specialized marketing knowledge graph model using pre-built knowledge graph information and pre-collected ontology information, and the marketing learning information and structured data (S109).

Then, the marketing information analysis service providing apparatus 100 performs marketing market trend and demand prediction analysis based on the marketing specialized knowledge graph model ( S111 ).

Here, the marketing information analysis service providing apparatus 100 may perform an analysis corresponding to the service according to the request of the user terminal 300, and may not only analyze the market trend and demand prediction, but also construct a neologism dictionary, analyze keyword influence, etc. This can be done further.

In addition, the marketing information analysis service providing apparatus 100 may provide marketing analysis information based on natural language processing according to the analysis result using the dashboard interface (S113).

4 is a block diagram for explaining in more detail a knowledge graph construction module according to an embodiment of the present invention.

Referring to FIG. 4 , in order to generate marketing-specific knowledge graph model data according to an embodiment of the present invention, unstructured data classified from marketing document data collected by the channel-based information collecting unit 140 and pre-collected structured data And, a large-capacity knowledge graph and ontology information obtained from the open knowledge graph may be required.

For example, the unstructured data for building a marketing-specialized knowledge graph model may be the original text of a marketing web page collected by the channel-based information collection unit 140, and the structured data may be a general-purpose file format or a structured data that can be collected through openAPI It may contain data. In addition, the open knowledge graph data may be domestic and foreign data published in RDF format, and may be obtained by receiving an RDF file or a query response targeting a SPARQL endpoint.

And, in order to build the marketing-specific knowledge graph model data from the collected or classified information, the knowledge graph construction module 151 processes step-by-step through a two-stage pipeline module as shown in FIG. 4 , thereby effectively marketing specialized knowledge Graph model building processing can be performed.

More specifically, referring to FIG. 4 , the knowledge graph building module 151 is a first pipeline module, and includes an unstructured data processing unit 1511 , a structured data processing unit 1512 , an open knowledge graph management unit 1515 , and a relational database. 1517 , and may include a natural language analysis unit 1513 , a knowledge graph information conversion unit 1514 , a large-capacity knowledge graph processing unit 1516 , and an ontology information processing unit 1518 as the second pipeline module.

In addition, the data output from the second pipeline may be transmitted to the marketing specialized knowledge graph construction unit 1519 and used to generate marketing specialized knowledge graph model data or keyword analysis information.

First, the unstructured data processing unit 1511 may identify unstructured data from the marketing document data collected in the first pipeline stage, and transmit it to the natural language analyzer 1513 . The unstructured data may include, for example, text data identified from marketing document data.

In addition, the natural language analysis unit 153 may extract main keywords using natural language processing technology from the unstructured data. The natural language processing technology may be exemplified by techniques such as morpheme analysis and entity name recognition, and the natural language analysis unit 1513 may use classification information of the document classification module 153 for more accurate keyword extraction processing.

Then, the extracted keyword may be transmitted to the knowledge graph information conversion unit 1514 . The knowledge graph information conversion unit 1514 is marketing knowledge graph information that maps and integrates into the knowledge graph information of a preset format by mapping technology such as rule-based marketing keyword mapping or machine learning algorithm-based mapping. Format conversion can be processed.

In addition, the open knowledge graph management unit 1515 may collect and store pre-built open knowledge graph information using an openAPI or the like.

And, the large-capacity knowledge graph processing unit 1516 pre-builds the large-capacity knowledge graph information prepared so that the collected open knowledge graph information can be mapped from the above-described natural language analysis information to the format-converted marketing knowledge graph information, and the built-in large-capacity The knowledge graph information may be transmitted to the marketing specialized knowledge graph model building unit 1519 .

On the other hand, the relational database 1517 may collect and store ontology information for semantic mapping between the knowledge graph information converted by the knowledge graph information conversion unit 1514 and the knowledge graph information processed by the large capacity knowledge graph processing unit 1516, Among the stored ontology information, mutually compatible ontology information may be transmitted to the marketing specialized knowledge graph construction unit 1519 .

For example, the marketing specialized knowledge graph model building unit 1519 collects open knowledge graph information collected from an RDF file or SPARQL Endpoint as knowledge graph model information for processing a large-capacity knowledge graph, and the converted marketing knowledge graph information By building a mapping table between and the large-capacity knowledge graph information, it is possible to build a marketing-specialized knowledge graph model.

To this end, the marketing-specialized knowledge graph model building unit 1519 performs mapping processing based on a unique identifier assigned to each data item, but in the case of the same data whose identifiers do not match, the pre-collected ontology information-based relationship information and attributes After calculating the coincidence probability through the information, data mapping processing for preferentially mapping the high probability may be performed.

5 is a flowchart illustrating an operation of a knowledge graph construction module according to an embodiment of the present invention, and FIG. 6 is a relationship diagram illustrating a knowledge graph construction and semantic mapping process according to an embodiment of the present invention.

5 and 6 are for explaining in more detail the process of generating the marketing-specific knowledge graph model data, and the knowledge graph building module 151, first as shown in FIG. 6, is a knowledge graph from OpenAPI or structured file data. Conversion rule information may be obtained (S201).

For example, the conversion rule information may be obtained from a conversion rule file described in R2RML (RDB to RDF Mapping Language), which is a W3C international standard. In addition, the transformation rule information may be converted into knowledge graph transformation rule data by using transformation rules described in RML (RDF Mapping Language) from OpenAPI or formatted file data.

Thereafter, the knowledge graph construction module 151 obtains ontology transformation rule information from the relational database (S203), and transforms the natural language analysis information of the unstructured data according to the knowledge graph transformation rule information (S205).

Thereafter, the knowledge graph construction module 151 maps the transformed knowledge graph information to a pre-built large-capacity knowledge graph according to the ontology transformation rule information to build a marketing-specialized knowledge graph model (S207).

As shown in FIG. 6 , the knowledge graph construction module 151 may include a marketing-specific knowledge graph model construction unit 1519 for generating marketing-specific knowledge graph model data. The marketing specialized knowledge graph model building unit 1519 may include the semantic mapping processing unit to efficiently perform the above-described mapping processing with high accuracy. Here, the semantic mapping processing unit may further include a fuzzy algorithm processing unit and a URI identifier processing unit.

More specifically, the semantic mapping processing unit may process semantic mapping between items of data converted into a knowledge graph format (eg, RDF) and a pre-established large-capacity knowledge graph item.

To this end, the semantic mapping processing unit may include a URI identifier processing unit for processing primary mapping by comparing URI identifiers assigned to all data items.

In addition, the semantic mapping processing unit applies a semantic mapping tool between words implemented based on the Levenshtein fuzzy metric algorithm developed according to the linguistic characteristics of Korean from the primary mapping-processed data to obtain automated meaning Mapping can be handled. The data on which the automatic mapping has been completed may be subjected to sampling processing, and the processed sampling data may be used for subsequent mapping inspection and correction processing.

Meanwhile, the knowledge graph construction module 151 may acquire knowledge graph model data on which semantic mapping is completed as marketing-specific knowledge graph model data. For example, the knowledge graph construction module 151 can create a knowledge graph model integrally by importing the mapping-processed knowledge graph data to a triplestore type database in which the large-capacity knowledge graph data previously built is stored. have.

Accordingly, it is preferable that the final knowledge graph model be described as an RDF (Resource Description Framework) data model, which improves compatibility and analysis efficiency.

In addition, the classification system for each item of the established large-capacity knowledge graph may be a marketing-specialized system created by a domain expert in the marketing field. By collecting and combining the brand and product classification system information of major portal services, it is possible to construct and process a more efficient large-capacity knowledge graph. To this end, the open knowledge graph management unit 1515 manages the classification system for each field based on the public interest (can be calculated as the number of searches for each period of the main portal service) corresponding to each classification system keyword, and the classification system for each field may decide to maintain or archive the

According to this knowledge graph model construction, the apparatus 100 for providing marketing information analysis service according to an embodiment of the present invention, the problem of not reflecting the latest keywords pointed out as a disadvantage of the general knowledge graph, the Korean-based knowledge graph and It solves the difficulties of building dictionary data for analysis, facilitates marketing trends and keyword analysis through the construction of a marketing-specialized knowledge graph model, and in particular, it reflects new words and facilitates analysis of Korean keywords to facilitate accurate marketing at a lower cost. It has the advantage of being able to provide information analysis services.

7 is a block diagram for explaining in more detail a real-time chat analysis service providing unit of a service providing unit according to an embodiment of the present invention, and FIG. 8 is a flowchart for explaining a service providing process according to an embodiment of the present invention.

As described above, the apparatus 100 for providing a live stream real-time chat content analysis service according to an embodiment of the present invention provides feedback information using a real-time chat function of a social media platform in order to effectively provide a marketing decision and analysis result. and a real-time chatting analysis service providing unit 171 capable of providing a marketing information service.

More specifically, the real-time chat analysis service providing unit 171 may provide immediate feedback by analyzing the text of mutual communication between the operator and a plurality of users through the real-time chat function provided by the social media platform. In addition, by using this, marketing based on optimal data for the purpose of the marketer can be presented to users through a visualized dashboard service in connection with marketing data.

To implement this, the real-time chat analysis service unit 171 according to an embodiment of the present invention for implementing this includes a user interface unit 1711, a collection unit 1712, a processing unit 1713, a real-time stream unit 1714 and and an analysis unit 1715 .

More specifically, the real-time chat analysis service providing unit 171 transmits the real-time chat text associated with the live stream input through the user interface unit 1711 to the real-time stream unit 1714, and the real-time stream unit 1714 may transmit the stored chat text to the processing unit 1713 . In this case, the real-time stream unit 1714 may directly transmit the stored chat text to the analysis unit 1715 .

The chat text classified and filtered through the processing unit 1713 may be transmitted to the analysis unit 1715 , and response information obtained from the analysis unit 1715 may be transmitted to the real-time stream unit 1714 . The real-time stream unit 1714 may process the analyzed content as visible content based on the analyzed chat text result information and output it from the user interface unit 1711 .

The collection unit 1712 collects user information for driving the real-time chatting function and transmits it to the processing unit 1713 . In order to collect user information, the collection unit 1712 processes the dashboard interface configured in the dashboard configuration unit 160 to be provided through the user interface unit 1711 , and the user collected through the user interface unit 1711 . information can be collected. The user may be an operator providing content through the social media platform and may also be a user for consuming content through the social media platform.

To collect user information, the collection unit 1712 may include a user information registration unit, a user data collection unit, a user log collection unit, and a knowledge base unit. The user information registration unit may receive and register login information for accessing social media from the user terminal 300 and information input by the user to activate a real-time chatting function.

The user data collection unit may collect the user's input information input through the interface unit 1711 , and the user log collection unit may collect the user's solution utilization information.

For example, the user information registration unit from the perspective of a social media operator includes, in addition to the basic login information of the user terminal 300 input through the interface unit 1711 , a company registered in social media, keywords related to content, SNS promotion channels, and online advertisements. It is possible to receive and store account information on media and product information sold in the e-commerce market, and furthermore, it is possible to set a target number for each online promotion and sales channel and store it as an index. To this end, the interface unit 1711 may provide a user setting information menu on the dashboard interface to facilitate the user's information registration. In addition, the user information registration unit from the perspective of the social media user may receive and store personal information such as social media account information in addition to the basic login information of the user terminal 300 input through the interface unit 1711 .

The user data collection unit may collect and store user-related information identified based on the marketing data stored in the user information registration unit online. In addition, the user data collection unit may collect the text and search volume written in the platform 200 of a predetermined channel based on the keyword input by the user.

For example, the user data collection unit may collect comparative data for marketing analysis of the own channel platform 200 and the platform 200 of a competitor's channel through account information of an SNS promotion channel and an online advertising medium. Also, for example, the user data collection unit may define and collect marketing data of its own and competitor product groups sold in the e-commerce market according to the analysis.

In addition, the user log collection unit may store and manage detailed records in which the user uses each function of the dashboard interface provided through the interface unit 1711 from login to logout. For example, the user log collection unit may track, store and manage the user learning log according to the marketer's solution user setting, utilization log, and calculation formula utilizing the dashboard of the marketing solution.

In addition, the knowledge base unit may include pre-built marketing-specific knowledge graph model information, which may be obtained from the model information built in the aforementioned knowledge graph construction module 151 .

Then, the processing unit 1713 may process the marketing-specialized knowledge graph model information into a required form, and group the user information, user data, and user log information to extract it as user information.

In addition, the processing unit 1713 receives the real-time chat text input from the user interface unit 1711 through the real-time stream unit 1714 and classifies the morpheme classification processing unit into words or word components representing grammatical or relational meanings. may include Additionally, the processing unit 1713 may further include a syntax filtering processing unit for filtering text classified as garbage data in the chat text.

Here, the morpheme classification processing unit may classify and process a morpheme, which is a unit of the smallest word having a meaning in the text. The classification of morphemes can be divided into independent morphemes or dependent morphemes depending on whether or not they can be used independently. In addition, depending on whether or not it has a substantial meaning, it may be classified into a real morpheme or a formal morpheme.

The character filtering processing unit may filter and process text determined as garbage data for incorrect text input such as a typo or not conforming to a text format.

In addition, the analysis unit 1715 may store the analyzed information by performing keyword analysis and sentiment analysis on the chat text data processed by the processing unit 1713 .

More specifically, the analysis unit 1715 may include a keyword analysis unit that generates keyword analysis information based on the real-time chatting text, through keyword extraction capable of identifying the user's intention or request.

Additionally, the analysis unit 1715 may further include a sentiment analysis unit that generates sentiment analysis information analyzed through text extraction capable of recognizing the user's emotions, based on the real-time chatting text. The keyword analysis information and sentiment analysis information obtained through the analysis unit 1715 may be transmitted to the real-time stream unit 1714 .

The real-time stream unit 1714 may include a real-time chat stream storage unit for storing user real-time chat text information input through the user interface unit 1711 . The stored chatting text information is transmitted to the processing unit 1713 or the analysis unit 1715 , and text analysis, information extraction, information classification, etc. may be performed using natural language processing technology or the like.

In addition, the real-time stream unit 1714 may further include a visualization content processing unit that processes to provide the visualized content to the user by utilizing the chat analysis result information analyzed based on the real-time chat text information. The visualization content may be expressed so that social media operators and users can intuitively check information extracted with the key keyword, or may provide marketing-related information related to the core keyword through an interface.

Additionally, the real-time stream unit 1714 may acquire real-time chat information through a stream API provided by YouTube, Facebook, etc. used as platforms of social media. In addition, content provided by a social media platform may be acquired through the stream API.

In particular, the keyword analysis unit according to an embodiment of the present invention may be designed to infer appropriate intentions and actions by assigning weights to web-based marketing and a keyword dictionary input by a user.

When this process is described with reference to FIG. 8 , first, the real-time chatting analysis service providing unit 171 collects real-time chatting text input information input through the user interface unit 1711 ( S401 ).

Then, the real-time chatting analysis service providing unit 171 transmits the chat text stored in the processing unit 1713 to the processing unit 1713 to perform pre-processing for detailed analysis of the chatting text (S403).

Thereafter, the processing unit 1713 transmits the pre-processed chat text information to the analysis unit 1715 to perform keyword analysis and sentiment analysis (S405).

The response information obtained through the keyword analysis and sentiment analysis is transmitted to the real-time stream unit 1714, processed as visualization content, and provided to the user interface 1711 (S407).

Accordingly, the user interface 1711 outputs visualized content information to the user (S411).

Meanwhile, the above-described method according to various embodiments of the present invention may be implemented as a program and provided to each server or device while being stored in various non-transitory computer readable media. Accordingly, the user terminal 100 may access the server or device to download the program.

The non-transitory readable medium refers to a medium that stores data semi-permanently, rather than a medium that stores data for a short moment, such as a register, cache, memory, and the like, and can be read by a device. Specifically, the above-described various applications or programs may be provided by being stored in a non-transitory readable medium such as a CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

In addition, although preferred embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above, and the technical field to which the present invention belongs without departing from the gist of the present invention as claimed in the claims In addition, various modifications are possible by those of ordinary skill in the art, and these modifications should not be individually understood from the technical spirit or perspective of the present invention.

Claims (12)

In the service providing device,
a collection unit for collecting user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information;
a processing unit for processing the information collected by the collection unit into structured data and unstructured data for marketing analysis;
an analysis unit for learning and analyzing the processed structured data or unstructured data; and
The real-time chat text associated with the live stream received from the user interface unit is transmitted to the analysis unit, and response information obtained from the analysis unit is obtained in response to the real-time chat text associated with the live stream, and the content visualized through the user interface unit is obtained. A real-time chat analysis service providing unit that outputs; including
Service providing device. According to claim 1,
The real-time chat analysis service providing unit,
Including a real-time stream unit;
The real-time stream unit,
a real-time chat stream storage unit for storing user real-time chat text information input through the user interface unit; and
A visualization content processing unit for processing the chat analysis result information analyzed based on the real-time chat text information as visualization content;
Service providing device. 3. The method of claim 2,
The analysis unit,
a keyword analysis unit that generates keyword analysis information through keyword extraction capable of identifying a user's intention or request based on the real-time chatting text; and
On the basis of the real-time chatting text, sentiment analysis unit for generating emotion analysis information analyzed through text extraction capable of grasping the user's emotion;
Service providing device. According to claim 1,
The collection unit,
Further comprising a user information registration unit for receiving marketing target company information and product information from the user terminal and registering as marketing data
Service providing device. 5. The method of claim 4,
Further comprising a user data collection unit that collects and stores user-related information identified based on the marketing data stored in the user information registration unit online
Service providing device. According to claim 1,
The knowledge base information includes marketing-specific knowledge graph model information obtained from the model information built in the knowledge graph construction module.
Service providing device. In the service providing method,
collecting user information of a user terminal receiving marketing information and knowledge base information using a marketing specialized knowledge graph model corresponding to the user information;
processing the information collected in the step of collecting the knowledge base information into structured data and unstructured data for marketing analysis;
an analysis step of learning and analyzing the processed structured data or unstructured data; and
The real-time chat text associated with the live stream received from the user interface unit is transmitted to the analysis unit, and response information obtained from the analysis unit is obtained in response to the real-time chat text associated with the live stream, and the content visualized through the user interface unit is obtained. A step of providing a real-time chat analysis service to output; including
HOW TO PROVIDE SERVICES. 8. The method of claim 7,
The step of providing the real-time chatting analysis service,
Including a real-time stream step;
The real-time stream step is
a real-time chat stream storage step of storing user real-time chat text information input through the user interface unit; and
A visualization content processing step of processing the chat analysis result information analyzed based on the real-time chat text information as visualization content;
HOW TO PROVIDE SERVICES. 9. The method of claim 8,
The analysis step is
a keyword analysis step of generating keyword analysis information based on the real-time chatting text, through keyword extraction capable of identifying a user's intention or request; and
Based on the real-time chatting text, sentiment analysis step of generating emotion analysis information analyzed through text extraction capable of grasping the user's emotion;
HOW TO PROVIDE SERVICES. 8. The method of claim 7,
The collecting step is
Receiving marketing target company information and product information from the user terminal, further comprising the step of registering as marketing data
HOW TO PROVIDE SERVICES. 11. The method of claim 10,
The method further comprising the step of collecting and storing user-related information identified based on the marketing data stored in the user information registration unit online
HOW TO PROVIDE SERVICES. 8. The method of claim 7,
The knowledge base information includes marketing-specific knowledge graph model information obtained from the model information built in the knowledge graph construction module.
HOW TO PROVIDE SERVICES.

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