KR20230000008A - Content publishing automation system using big data - Google Patents

Abstract

The present invention is to provide a big data-based content publishing automation system (100) which automatically collects large amounts of big data, which is useful information people need in daily lives, in a short period of time, process the collected big data, and dynamically create and post website pages without need for a person in charge to write, edit, or post the website pages directly. The big data-based content publishing automation system comprises: a data mining engine (110) which collects online data related to specific government offices in parallel based on multi-process; a natural language processor (120) which extracts words and phrases related to a region where a government office is located and regional policies related to the government office through natural language processing of data; a data analyzer (130) which extracts and classifies words and phrases extracted by the natural language processor (120) into content units, and stores the classified words and phrases; and a content publisher (140) which automatically generates posts to be posted on the web page of the government office by using the words and phrases classified and stored as content units in the data analyzer (130), and actually posts the generated posts on the web page.

Description

Content publishing automation system using big data}

It relates to a big data-based content publishing automation system that automatically collects big data, processes the collected big data, and dynamically creates and posts website pages.

Currently, with the development of information and communication technology and the spread of PCs and smartphones, many tasks of local governments and public institutions are being performed online.

Civil petitioners request civil petitions online using their PCs or smartphones to government agencies and public institutions, including local governments, without directly visiting public offices. Notifying complainants.

However, as the number of civil petitions requested online increases, public officials have to take charge of website operation and management in addition to their own duties, including public affairs, civil complaint handling, and policy development. Due to this increasing problem, it is necessary to develop a system capable of dynamically creating, posting, and managing website pages.

The present invention has been made to solve the above problems, and automatically collects a large amount of big data in a short time for useful information that the people need in their daily life, processes the collected big data, and writes and edits it directly by the person in charge. However, its purpose is to provide a big data-based content publishing automation system that dynamically creates and posts website pages without posting them.

A data mining engine that collects online data related to a specific government office in parallel based on the big data-based content publishing automation system of the present invention, Multi-Process; a natural language processor for extracting words and phrases associated with the region where the government office is located and local policies related to the government office by natural language processing of the data; a data analyzer for extracting and classifying the words and phrases extracted by the natural language processor in units of content, and storing the classified words and phrases; and a content publisher that automatically creates a posting to be posted on the webpage of the government office by utilizing the words and phrases classified and stored in the data analyzer by content unit and actually posts the posting on the webpage.

In addition, the data mining engine includes an external interface connecting to a plurality of external servers on-line; a collection policy manager that determines data to be collected from the external server and a collection schedule; a crawler crawling data to be collected from the external server; a crawler handler controlling execution of the crawler by determining a policy for driving the crawler in multi-process; and a data extractor extracting content from the data collected by the crawler.

In addition, it is characterized in that the number of crawlers driven by multi-process is twice the number of CPU cores.

In addition, it is characterized in that the number of crawlers driven by multi-process is 2 to 4.

In addition, the crawler compares the data to be collected and the data previously collected and stored in the database based on the URL, and if the data to be collected is determined to be the same as the previously collected and stored data, the telegram module excludes the data from the data to be collected. In addition, the data to be collected by the crawler from the external server may include images, videos, attachments, and comments in the web page.

In addition, the crawler handler includes an execution module that executes the crawler in multi-process; a scheduling module that drives the execution module according to the collection schedule set by the logging policy manager; and a log file for generating a log file recording log data including the driving start time and driving end time of the execution module, whether or not the execution module was successful or failed, and the amount of data collected from the external server by the execution module. A generation module; may be included.

In addition, the natural language processor includes a natural language preprocessor outputting data collected by the data mining engine as morphemes capable of natural language analysis; a morpheme analyzer that analyzes the morpheme output from the natural language preprocessor; a dictionary DB that can be referred to when morpheme analysis is performed by the morpheme analyzer and can be updated by a user; a keyword extractor extracting keywords from the morphemes analyzed by the morpheme analyzer according to preset rules; An analysis manager managing the natural language preprocessor, the morpheme analyzer, the dictionary DB, and the keyword extractor; may be included.

In addition, the data analyzer includes a content classifier for classifying and grouping words and phrases that can be used as content among words and phrases extracted by the natural language processor; a content extractor for automatically extracting a content candidate group that can be posted on a specific web page of the public office from words and phrases classified and grouped by the content classifier; a content storage for storing words and phrases extracted by the content extractor and storing image, video, and link information among data collected by the data mining engine; and a content manager that manages the content classifier, the content extractor, and the content repository, and determines criteria for classifying and grouping the words and phrases in the content classifier and criteria for extracting the content candidate group in the content extractor. there is.

In addition, the intelligent content publisher includes a content template manager for creating and managing a template of a specific web page to be posted on the government office homepage; a content creator generating content to be posted on the specific webpage by utilizing the words and phrases extracted by the natural language processor and the template generated by the content template manager; a distribution manager that creates and manages settings for posting the content to the specific web page; In order to post the content on the specific web page, an internal interface for accessing and communicating with a web server managing the homepage of the government office; may be included.

The big data-based content publishing automation system of the present invention can also reduce website-related tasks in addition to the unique tasks of public officials.

In addition, it is possible to collect big data quickly by collecting big data using a multi-process crawler and using the collected big data to create a web page.

In addition, it is possible to create a web page automatically, thereby reducing the cost required for website management.

1 is a conceptual diagram of a content publishing automation system based on big data of the present invention.
2 is a conceptual diagram of the data mining engine of the present invention.
3 is a conceptual diagram of the natural language processor of the present invention.
4 is a conceptual diagram of the natural language preprocessor of the present invention.
5 is a conceptual diagram of the morpheme analyzer of the present invention.
6 is a conceptual diagram of a keyword extractor of the present invention.
7 is a conceptual diagram of an analysis manager of the present invention.
8 is a conceptual diagram of the data analyzer of the present invention.
9 is a conceptual diagram of a content publisher of the present invention.
10 is a conceptual diagram of a content template manager according to the present invention.
11 is a conceptual diagram of a content creator of the present invention.
12 is a conceptual diagram of a distribution manager of the present invention.

Since the present invention can make various changes and have various embodiments, specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The term "and/or" includes any combination of a plurality of related listed items or any of a plurality of related listed items.

It is understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, but other elements may exist in the middle. It should be. On the other hand, when an element is referred to as “directly connected” or “directly connected” to another element, it should be understood that no other element exists in the middle.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

In this regard, the terms "about," "substantially," and the like, as used throughout the specification, are used at or approximating that value when manufacturing and material tolerances inherent in the stated meaning are given, and herein Exact or absolute figures are used to aid understanding of the invention and to prevent unfair use by unscrupulous infringers of the disclosed disclosure. The term "step of (doing)" or "step of" used throughout the specification of the present invention does not mean "step for".

In this specification, a "unit" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Further, one unit may be realized using two or more hardware, and two or more units may be realized by one hardware.

In this specification, some of the operations or functions described as being performed by a terminal, device, or device may be performed instead by a server connected to the terminal, device, or device. Likewise, some of the operations or functions described as being performed by the server may also be performed by a terminal, apparatus, or device connected to the server.

In this specification, some of the operations or functions described as mapping or matching with a terminal are interpreted as meaning mapping or matching a terminal's unique number or personal identification information, which is the terminal's identifying data. It can be.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in this application, they should not be interpreted in an ideal or excessively formal meaning. don't

Hereinafter, with reference to the accompanying drawings, preferred embodiments of the present invention will be described in more detail. In order to facilitate overall understanding in the description of the present invention, the same reference numerals are used for the same components in the drawings, and redundant descriptions of the same components are omitted.

The present invention automatically collects a large amount of big data of useful information that people need in daily life in a short time, processes the collected big data, and dynamically creates website pages without the person in charge directly writing, editing, or posting. It relates to a big data-based content publishing automation system 100 that creates and publishes content.

1 is a conceptual diagram of a content publishing automation system 100 based on big data of the present invention. Referring to FIG. 1, the big data-based content publishing automation system 100 of the present invention accesses a plurality of external servers 10 through an external interface 111 to collect online data related to a specific government office, After processing the data so that it can be posted on a specific web page of the homepage of the government office, access the web server 20 for the homepage of the government office through the internal interface 144, upload the web page, and automatically post it. there is.

To this end, the present invention may include a data mining engine 110, a natural language processor 120, a data analyzer 130, and a content publisher 140.

2 is a conceptual diagram of the data mining engine 110 of the present invention. Referring to FIG. 2 , the data mining engine 110 may collect online data related to a specific public office in parallel based on a multi-process. To this end, the data mining engine 110 may include an external interface 111, a logging policy manager 112, a crawler 113, a crawler handler 114, and a data extractor 115.

The external interface 111 creates a communication path capable of connecting to a plurality of external servers 10 on-line, and the data mining engine 110 accesses the external server 10 through the communication path to provide information related to the government office. data can be collected.

At this time, the external interface 111 can receive data from the external server 10 by any one method selected from among RSS, OpenAPI, HTML5, Meta, File, and RDB methods, and the external server 10 ) may be any one selected from a social media server, a server providing public data, and a server providing public data.

The collection policy manager 112 may determine data to be collected from the external server 10 and a collection schedule. Keywords included in the data to be collected by the crawler 113 may be registered, modified, and deleted, and the collection schedule including the operating cycle and period of the data mining engine may be set and managed.

The crawler 113 may crawl data to be collected from the external server 10 . The crawler 113 compares the data to be collected and the data previously collected and stored in the database based on the URL, and if the data to be collected is determined to be the same as the previously collected and stored data, the telegram module excludes the data from the data to be collected. Including, the data to be collected by the crawler 113 from the external server 10 may include images, videos, attachments, and comments in the web page.

When the telegram module collects data for which the condition or title of the content to be collected is updated, the first title of the title of specific data of the external server 10 corresponding to the data to be collected is imported and the txt extension is added. The branch is stored as a "title file", and the URL address of the specific data is created and stored as a separate "address file" having a txt extension in the same folder as the "title file".

In this case, the title of the most recent article is stored in the "title file", so that the title stored in the "title file" and the title of the re-searched data are the same in order to check whether the data re-searched after crawling is duplicated. By checking, if the title is the same, the re-searched data is not collected, and if not, the re-searched data is collected to prevent duplicate data collection.

Whereas the single-process crawler takes about 7 seconds for a unit task, the clolor of the present invention is multi-process, so the time required for multi-processing parallel crawling for the same task is about 2 seconds. There is an effect of improving the speed by 3 to 4 times by shortening it.

However, it was confirmed that the time required for crawling by the crawlers for the same file decreases whenever the number of simultaneously performed crawlers doubles. Rather, it was confirmed that the speed was delayed.

As the number of crawlers performed in parallel increases by 2 times, the section where the crawling speed improves can be determined by the hardware specifications used in this system. This would ensure a stable and fast speed.

Therefore, the multi-process crawler 113 used in the system of the present invention is performed by doubling the number of CPU cores in the system of the present invention to maintain a stable system environment, preferably a stable network environment When this is guaranteed, the optimal speed can be obtained by crawling with the number of crawlers 113 driven in parallel being 2 to 4.

Substantially, the crawler 113 may be controlled by the crawler handler 114. The crawler handler 114 determines a policy for driving the crawler 113 in a multi-process and determines the You can control the execution.

To this end, the crawler handler 114 includes an execution module 114a that executes the crawler 113 in multi-process and drives the execution module 114a according to the collection schedule set by the logging policy manager 112. The scheduling module 114b and the driving start time and driving end time of the execution module 114a, whether the execution module 114a was successful or failed, collected from the external server 10 by the execution module 114a It may include a log generation module 114c for generating a log file in which log data including the amount of recorded data is recorded.

The data extractor 115 may extract content from data collected by the crawler 113 . To this end, it may include a first data extraction module 115a, a second data extraction module 115b, an indexing module 115c, and a storage management module 115d.

The first data extraction module 115a separates a content area and a non-content area from the data collected by the crawler 113 and extracts only data from the content area, and the second data extraction module 115b extracts data from the content area. Of the data extracted by the content extraction module, metadata or hashtags included in web pages and social media contents can be automatically extracted, and the indexing module 115c is used to extract the first data extraction module 115a and the second data extraction module 115c. The data extracted by the data extraction module 115b is automatically indexed by object of collection and content, and the storage management module 115d stores and manages the data indexed by the indexing module 115c in a database.

3 is a conceptual diagram of the natural language processor 120 of the present invention, and FIGS. 4 to 7 show the natural language preprocessor 121, the morpheme analyzer 122, the keyword extractor 124, and analysis of the natural language processor 120, respectively. It is a conceptual diagram of the manager 125.

The natural language processor 120 may extract words and phrases associated with a region where the government office is located and a regional policy related to the government office by natural language processing of the data. To this end, the natural language processor 120 may include a natural language preprocessor 121, a morpheme analyzer 122, a dictionary DB 123, a keyword extractor 124, and an analysis manager 125.

The natural language preprocessor 121 may output data collected by the data mining engine 110 as morphemes capable of natural language analysis.

Referring to FIG. 4, the natural language preprocessor 121 includes a data purification module 121a that excludes HTML tags, special characters, and emoticons from the data collected from the external server 10, phrases out of basic grammar, and typos. A normalization module 121b for extracting and excluding from the data, a division module 121c for separating sentences included in the data into lexical units and dividing them into parts-of-speech units for each of the vocabularies, A POS (Part Of Speach) tagging module 121d for matching vocabulary with corresponding parts of speech, a stem extraction module 121e for converting vocabulary corresponding to verb tenses and honorific words among the vocabulary into representative verbs, and the stem In the extraction module 121e, a word processing module 121f for homonyms with a risk of distorting meaning when converted into representative verbs and a stopword removal module 121g for removing words whose meaning is unclear or meaningless among the vocabulary words are provided. can include

The morpheme analyzer 122 may analyze the morpheme output from the natural language preprocessor 121. Referring to FIG. 5, the morpheme analyzer 122 includes a first morpheme module 122a and a second morpheme module 122b. can include The first morpheme module 122a recognizes the basic form and parts of speech of the morpheme, and the second morpheme module 122b decomposes the morpheme into components of the sentence to determine the structure of the sentence.

The dictionary DB 123 can be referred to when morpheme analysis is performed by the morpheme analyzer 122 and can be updated by a user, and the dictionary DB 123 for this purpose includes a morpheme dictionary DB 123 and an object name dictionary DB 123 , region/policy dictionary DB 123, and user-defined dictionary DB 123.

The keyword extractor 124 may extract keywords from the morphemes analyzed by the morpheme analyzer 122 according to preset rules. Referring to FIG. , a second extraction module 124b, a third extraction module 124c, and a fourth extraction module.

The first extraction module 124a may extract keywords from the morphemes analyzed by the morpheme analyzer 122 according to a value set by the analysis manager 125, and the second extraction module 124b may extract the extracted keywords. The keyword can be scored according to the frequency and the degree of association with the region related to the public office by using the dictionary DB 123, and the third extraction module 124c performs the related word according to the score determined by the rating module. and phrases can be extracted.

The analysis manager 125 may manage the natural language preprocessor 121, the morpheme analyzer 122, the dictionary DB 123, and the keyword extractor 124. Referring to FIG. 7, the analysis manager 125 includes a first manager module 125a and a second manager module 125b, and the first manager module 125a can upload and download the dictionary DB 123, Contents can be searched, added, modified, and deleted, and the second manager module 125b sets conditions including a specific region for extracting and analyzing the morphemes in the natural language preprocessor 121 and morpheme analyzer 122. can

8 is a conceptual diagram of the data analyzer 130 of the present invention. The data analyzer 130 extracts and classifies words and phrases extracted by the natural language processor 120 in units of content, and classifies the classified words and phrases. can be saved

Referring to FIG. 8 , the data analyzer 130 may include a content classifier 131, a content extractor 132, a content storage 133, and a content manager 134.

The content classifier 131 may classify and group words and phrases usable as content among words and phrases extracted by the natural language processor.

The content extractor 132 can automatically extract a content candidate group that can be posted on a specific web page of the government office from words and phrases classified and grouped by the content classifier 131 .

The content storage 133 may store words and phrases extracted by the content extractor 132 and store image, video, and link information among data collected by the data mining engine 110 .

The content manager 134 manages the content classifier 131, the content extractor 132, and the content storage 133, and the criteria for classifying and grouping the words and phrases in the content classifier 131 and the content extractor In step 132, a criterion for extracting the content candidate group may be determined.

Using the words and phrases classified and stored in the data analyzer 130 by content unit, posts to be posted on the webpage of the government office can be automatically created and actually posted on the webpage. A conceptual diagram of the content publisher 140 is simply shown. Referring to FIG. 9, the content publisher 140 may include a content template manager, a content creator 142, a distribution manager 143, and an internal interface 144, and FIGS. 10 to 12 each represent the content. It is a conceptual diagram of the publisher 140, content template manager, content creator 142, and distribution manager 143.

The content template manager may create and manage a template of a specific web page to be posted on the government office homepage. To this end, the content template manager determines a content structure including paragraphs and arrangement methods of the content. A first template module ( 141a), a second template module 141b for determining the location of the content according to the content structure set in the first template module 141a, and a third template module 141c for determining the font used for the content. ), and a fourth template module 141d for determining the content layout.

In this case, the content layout may be one selected from among a text layout composed of only text, a first mixed layout composed of a mixture of text and images, and a second mixed layout composed of a mixture of text and images.

The content generator 142 may create content to be posted on the specific web page by utilizing the words and phrases extracted by the natural language processor and the template generated by the content template manager.

To this end, a first generation module 142a automatically generates keywords and phrases to be used in the content, and a first generation module 142a automatically completes sentences suitable for the content using the keywords and phrases generated in the first generation module 142a. 2 generation module 142b, a 3rd generation module 142c that automatically completes the skeleton of a sentence by utilizing the sentence completed in the second generation module 142b, and the sentence generated by the second generation module 142b A fourth generation module (142d) that automatically verifies and corrects grammatical errors in and automatically converts the content including the corrected sentences into HTML tags. A fifth generation module 142e may be included.

The distribution manager 143 can create and manage settings for publishing the contents on the specific web page, and for this purpose, the first distribution module 143a reviews and modifies the contents generated by the contents generator 142. ), a second distribution module 143b for determining a web page on which the content is to be posted from among the web pages of the homepage of the government office and executing the posting, a third distribution module 143c for managing the posting schedule of the content, the content A fourth distribution module (143d) for storing and managing the posting history of and a fifth distribution module (143e) generating a dashboard showing statistical values for the posting history of the content stored by the fourth distribution module (143d). A first distribution module 143a for reviewing and modifying the content generated by the content generator 142, and a second distribution module for determining a web page on which the content is to be posted among web pages of the homepage of the government office and publishing the content ( 143b), the third distribution module 143c for managing the posting schedule of the contents, the fourth distribution module 143d for storing and managing the posting history of the contents, and the contents stored by the fourth distribution module 143d. It may include a fifth distribution module 143e that generates a dashboard indicating statistical values for the posting history of .

The internal interface 144 may access and communicate with the web server 20 that manages the government office homepage in order to post the content on the specific web page. That is, the big data-based content publishing automation system 100 of the present invention automatically uploads the content to the web server 20 through the internal interface 144, so that it can be posted to a specific web page of the government office. will be. At this time, the internal interface 144 can use a separate internal communication network unlike the communication network connected to the external server 10, and the internal communication network uses an optical cable, a 10G Ethernet network, and the like, depending on the environment of the system. can be configured.

In addition, by using the multi-process crawler 113, a large amount of big data can be automatically collected in a short period of time, useful information needed by the people in daily life compared to the conventional single-process crawler 113. In addition, by processing the collected big data, the person in charge can dynamically create and post website pages without directly writing, editing, or posting.

Through this, there is an effect that public officials of the public office can focus on their own tasks, thereby reducing civil petition processing time and reducing costs required for website management.

The present invention is not limited to the specific preferred embodiments described above, and various modifications can be made by anyone skilled in the art without departing from the gist of the present invention claimed in the claims. Of course, such changes are within the scope of the claims.

10: external server
20: Web server
100: Big data-based content publishing automation system
110: data mining engine
120: natural language processor
130: data analyzer
140: Content Publisher

Claims (9)

A data mining engine 110 that collects online data related to a specific government office in parallel based on a multi-process;
a natural language processor (120) for extracting words and phrases associated with the region where the government office is located and local policies related to the government office by natural language processing of the data;
a data analyzer 130 that extracts and classifies the words and phrases extracted by the natural language processor 120 in units of content, and stores the classified words and phrases; and,
A content publisher 140 that automatically creates a post to be posted on the webpage of the government office by using the words and phrases classified and stored in the data analyzer 130 by content unit and actually posts it on the webpage. Big data based content publishing automation system. According to claim 1,
The data mining engine 110,
an external interface 111 connecting to a plurality of external servers 10 on-line;
a logging policy manager 112 that determines data to be collected from the external server 10 and a collection schedule;
a crawler 113 for crawling data to be collected from the external server 10;
a crawler handler 114 for controlling execution of the crawler 113 by determining a policy for driving the crawler 113 in multi-process; and,
Data extractor 115 for extracting content from the data collected by the crawler 113; Big data-based content publishing automation system including a. According to claim 2,
Big data-based content publishing automation system, characterized in that the number of crawlers 113 driven by multi-process is twice the number of CPU cores. According to claim 2,
Big data-based content publishing automation system, characterized in that the number of crawlers 113 driven by multi-process is 2 to 4. According to claim 2,
The crawler 113 compares the data to be collected and the data previously collected and stored in the database based on the URL, and if the data to be collected is determined to be the same as the previously collected and stored data, the telegram module excludes the data from the data to be collected. Including, the data to be collected by the crawler 113 from the external server 10 includes images, videos, attachments, and comments in the web page. Big data-based big data-based content publishing automation system. According to claim 2,
The crawler handler 114,
Execution module 114a for executing the crawler 113 in multi-process;
a scheduling module 114b for driving the execution module 114a according to the collection schedule set by the logging policy manager 112; and,
Includes the driving start time and driving end time of the execution module 114a, whether the execution module 114a has been driven successfully or failed, and the amount of data collected from the external server 10 by the execution module 114a. A big data-based content publishing automation system including; a log generation module (114c) for generating a log file recording log data to According to claim 1,
The natural language processor 120,
a natural language preprocessor 121 outputting the data collected by the data mining engine 110 as morphemes capable of natural language analysis;
a morpheme analyzer 122 that analyzes the morpheme output from the natural language preprocessor 121;
a dictionary DB 123 that can be referred to when morpheme analysis is performed by the morpheme analyzer 122 and can be updated by a user;
a keyword extractor 124 for extracting keywords from the morphemes analyzed by the morpheme analyzer 122 according to preset rules;
An analysis manager 125 managing the natural language preprocessor 121, the morpheme analyzer 122, the dictionary DB 123, and the keyword extractor 124; Big data-based content publishing automation system including. According to claim 1,
The data analyzer 130,
a content classifier 131 for classifying and grouping words and phrases usable as content among the words and phrases extracted by the natural language processor;
a content extractor 132 that automatically extracts a content candidate group that can be posted on a specific web page of the government office from among words and phrases classified and grouped by the content classifier 131;
a content storage 133 for storing words and phrases extracted by the content extractor 132 and storing image, video, and link information among data collected by the data mining engine 110; and,
The content classifier 131, the content extractor 132, and the content storage 133 are managed, and the criteria for classifying and grouping the words and phrases in the content classifier 131 and the content candidate group in the content extractor 132 Big data-based content publishing automation system including; content manager 134 for determining criteria for extracting. According to claim 1,
The content publisher 140,
a content template manager that creates and manages a template of a specific web page to be posted on the government office homepage;
a content generator 142 generating content to be posted on the specific webpage by utilizing the words and phrases extracted by the natural language processor and the template generated by the content template manager;
a distribution manager 143 that creates and manages settings for posting the content to the specific web page;
An internal interface 144 that connects to and communicates with the web server 20 that manages the government office homepage in order to post the content on the specific web page; Big data-based content publishing automation system including.

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