KR101523746B1 - Method for providing a map based on sensitivity information - Google Patents

Abstract

A method for providing emotion information based map according to an embodiment of the present invention includes the steps of receiving map information from a terminal, extracting local information from the map information, Wherein the server generates emotional map information based on the map information and the representative emotional information, and transmits the emotional map information to the terminal As shown in FIG.

Description

METHOD FOR PROVIDING A MAP BASED ON SENSITIVITY INFORMATION

An embodiment according to the concept of the present invention relates to a map providing method, and more particularly, to a map providing method for displaying collective emotion information about an area displayed on a map.

As information and communication technology develops, maps are provided through the Internet web or map providing program. Unlike a map printed on a paper, a map provided through a computer can control the resolution or scale of the map through manipulation, thereby improving the user's convenience.

In addition, a map provided through a computer can display additional information together. For example, a map provided through a computer can display information about a restaurant, a movie theater, or a sightseeing spot together.

However, a map provided through a conventional computer does not include emotion information corresponding to the area. Therefore, there is a need for a description of a map providing method in which additional information is provided according to the group sensibility information of the netizen in the region.

SUMMARY OF THE INVENTION The present invention has been made in view of the above problems, and it is an object of the present invention to provide a method of providing a map displaying net emotion group sensibility information about a point displayed on a map.

A method for providing emotion information based map according to an embodiment of the present invention includes the steps of receiving map information from a terminal, extracting local information from the map information, Wherein the server generates emotional map information based on the map information and the representative emotional information, and transmits the emotional map information to the terminal As shown in FIG.

The collecting of the contents may allow the server to collect the contents marked with the local information.

In the collecting of the contents, the server may collect contents including location information of an area corresponding to the area information.

The emotion map information may include the representative emotion information corresponding to the local information in a region on the map.

The emotion map information may be classified and displayed on the map according to the representative emotion information.

Further comprising the step of the server collecting contents generated by the user of the terminal from at least one external database and deducing the user emotion information for the user from the collected contents, The emotion map information may be generated based on the map information, the representative emotion information, and the user emotion information, and the emotion map information may be transmitted to the terminal.

Wherein the emotion map information includes at least one of the local information corresponding to the representative emotion information associated with the user emotion information and the emotion path information linked with the local information corresponding to the representative emotion information related to the user emotion information .

The emotion information-based map providing method according to the embodiment of the present invention can be stored in a computer-readable recording medium that is created by a computer program.

The method of providing emotional information display map according to the embodiment of the present invention has an effect of providing a map in which emotional information about an area corresponding to a map is displayed.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS In order to more fully understand the drawings recited in the detailed description of the present invention, a detailed description of each drawing is provided.
FIG. 1 shows a system for providing emotion information based maps according to an embodiment of the present invention and a schematic data flow used in the system.
2 is a flowchart illustrating an emotion information inference method performed by the server shown in FIG.
FIG. 3 shows emotion map information according to an embodiment of the present invention.

It is to be understood that the specific structural or functional descriptions of embodiments of the present invention disclosed herein are only for the purpose of illustrating embodiments of the inventive concept, But may be embodied in many different forms and is not limited to the embodiments set forth herein.

Embodiments in accordance with the concepts of the present invention are capable of various modifications and may take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. It should be understood, however, that it is not intended to limit the embodiments according to the concepts of the present invention to the particular forms disclosed, but includes all modifications, equivalents, or alternatives falling within the spirit and scope of the invention.

The terms first, second, or the like may be used to describe various elements, but the elements should not be limited by the terms. The terms are intended to distinguish one element from another, for example, without departing from the scope of the invention in accordance with the concepts of the present invention, the first element may be termed the second element, The second component may also be referred to as a first component.

It is to be 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, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Other expressions that describe the relationship between components, such as "between" and "between" or "neighboring to" and "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", or the like, specify that there is a stated feature, number, step, operation, , Steps, operations, components, parts, or combinations thereof, as a matter of principle.

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 this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, the present invention will be described in detail with reference to the preferred embodiments of the present invention with reference to the accompanying drawings.

FIG. 1 shows a system for providing emotion information based maps according to an embodiment of the present invention and a schematic data flow used in the system, and FIG. 3 shows emotion map information according to an embodiment of the present invention.

Referring to FIGS. 1 and 3, the emotional information-based map providing system 10 includes a terminal 100 and a server 200.

The terminal 100 may transmit the map information (GI) for the displayed map to the server 200 (S100).

According to an embodiment, the terminal 100 may transmit map information (GI) for the point selected by the user or map information (GI) for all the points displayed on the displayed map to the server 200. [

The server 200 may extract the area information AI from the map information GI received from the terminal 100 (S110).

For example, the area information AI may be a keyword related to a name, a location, or an address corresponding to the map information GI. In addition, the area information (AI) may include a city, a city, a district, and a county, which means a country, a city, and an administrative district.

The server 200 may collect the contents CNT related to the local information AI from the database DB1 at step S120.

For example, the server 200 may collect contents (CNT) marked with local information (AI) from the database DB1. Also, the server 200 may collect the contents CNT including the location information of the area corresponding to the area information AI.

The database DB1 may include contents such as text information, multimedia information, and various types of digital information.

According to the embodiment, the database DB1 may be included in the server 200 or may be an external database.

According to an embodiment, the database DB1 may be an external database that stores content collected by a web crawler.

According to the embodiment, the database DB1 may be connected to a plurality of other databases (not shown). For example, the plurality of databases may include an e-mail database capable of storing e-mails, a database capable of storing messages according to short message service (SMS), a database capable of storing messages according to enhanced messaging service (EMS) A database capable of storing messages according to a multimedia message service (MMS), a database capable of storing messages according to a social network service (SNS), or a database capable of storing a blog.

That is, the content CNT may include text-based contents, digital information, still images, moving images, sound sources, and advertisement images that can be provided through a wired / wireless communication network.

The server 200 can infer the representative emotion information (RSI) about the collected contents (CNT) (S130).

More specifically, the server 200 analyzes each of the collected contents (CNTs), maps each of the collected contents (CNTs) based on the analysis result to a predetermined emotion class, and collects The emotion information for each of the contents CNTs can be inferred.

For example, the server 200 extracts an emotional vocabulary from content (CNT) related to the local information (AI), compares the emotional vocabulary with the emotion experience dictionary, You can determine the emotional class for.

The emotion experience dictionary is a self-expanding dictionary in which a crawler automatically finds and updates a new vocabulary on the Internet when a new vocabulary is found from a sentence input by the user, the dictionary being analyzed and stored for analysis of vocabulary for emotion information extraction.

The emotion experience dictionary includes information on a circle, part of speech, emotion class, affinity, valence, arousal, or utilization of a vocabulary.

According to the embodiment, the server 200 can deduce the emotion information from the contents including the location information of the area corresponding to the local information (AI). For example, the SNS has the capability to include location information in the message when the user enters the message. The server 200 deduces emotion information from the messages including the position information and generates representative emotion information (RSI) for the local information corresponding to the position information based on the emotion information inferred, for example, representative emotion and emotion intensity Can be deduced. At this time, the server 200 can collect keywords that have an influence on the representative emotion information (RSI) reasoning.

According to another embodiment, the server 200 can deduce emotion information from contents marked with local information (AI). For example, the server 200 can deduce emotional information about Samsung Dock or COEX (local information) from the message 'I ate dirty rice today at Samsung-dong COEX.' The emotion information is inferred from each of the contents in which the local information (AI) is indicated, and the representative emotion information (RSI) for the local information (AI) can be inferred based on the inferred emotion information.

The process by which the server 200 deduces the representative emotion information (RSI) for the contents CNT will be described in detail in Fig.

The server 200 can generate emotion map information SMI based on the representative emotion information RSI and the map information GI (S140).

According to an embodiment, the server 200 collects content generated by a user of the terminal 100 from at least one external database (not shown), and deduces user emotion information for the user from the collected content . In addition, the server 200 can generate emotion map information SMI based on the map information GI, representative emotion information RSI, and the user emotion information. For example, the process of inferring the user emotion information is substantially the same as the process of inferring the representative emotion information (RSI), which will be described in detail with reference to FIG.

According to the embodiment, the emotion map information SMI may include related information related to the representative emotion information RSI, for example, an event that caused the representative emotion information RSI to be generated, or information about the person, As shown in FIG. For example, the server 200 may analyze the contents (CNT) to retrieve the related information.

According to another embodiment, the emotion map information SMI may include related contents related to the representative emotion information RSI, for example, similar regional information, restaurant information, movie information, music information, photograph information, or book information.

According to another embodiment, the emotion map information (SMI) can be displayed by classifying the areas on the map according to the representative emotion information (RSI).

According to another embodiment, the emotion map information SMI may be displayed with representative emotion information (RSI) corresponding to the local information AI on a map by region.

According to another embodiment, the emotional guidance information SMI may include local information AI corresponding to the representative emotion information RSI associated with the user emotion information, and representative emotion information RSI associated with the user emotion information And emotion path information to which local information (AI) is connected.

For example, the representative emotion information (RAI) associated with the user emotion information may mean emotion information similar or opposite to the user emotion information. Therefore, the emotional path information may refer to a driving course, which connects areas having emotion information similar to or opposite to the user emotion information.

The server 200 may transmit the generated emotion map information SMI to the terminal 100 (S150).

The terminal 100 may display the emotion map information SMI received from the server 200 on the display of the terminal 100 (S160).

In other words, the emotion map information (SMI) can be expressed on the map as a color, an icon, or text, and the representative emotion information (RSI) can be displayed by moving the user's input, The related information or the associated content may be displayed.

2 is a flowchart illustrating an emotion information inference method performed by the server shown in FIG.

Referring to FIG. 2, the server 200 stores lexical information including at least one of emotional class, affinity, affirmative or negative, and emotional intensity for each of the vocabularies (S200).

Emotional class represents a classification of human internal emotional states such as satisfaction, longing, happiness, Similarity refers to the degree of similarity between each of the vocabularies and items in the emotional class, and can be expressed as a numerical value within a certain range. Positive or negative is a measure of whether the attribute of the vocabulary is positive emotion or negative emotion, and can be expressed as a negative or positive number within a certain range based on zero. The emotion intensity represents the intensity of emotion in the attributes of the corresponding vocabulary, and can be expressed in numerical values within a certain range.

The server 200 deduces emotion information about the collected contents by applying a probabilistic model based on co-occurrences of vocabularies contained in the collected contents (CNT). The server 200 uses the stored lexical information to objectively quantify emotion information (S210).

Specifically, the server 200 calculates emotion similarity for a specific emotion of the collected contents CNT according to Equation (1).

[Equation 1]

Here, AR is the emotional intensity of emotion corresponding to vocabularies included in the collected contents (CNT), and AR 'corresponds to vocabularies belonging to specific emotions among the vocabularies contained in the collected contents (CNT) It is the emotion intensity for a specific emotion.

The server 200 calculates emotion similarity for all the emotion classes according to Equation (1) above, and determines the emotion class having the greatest emotion similarity to the representative emotion according to the calculation result.

In addition, the emotion intensity for the representative emotion of each of the collected contents (CNT) is determined according to the following equation (2).

&Quot; (2) "

Here, N (AR ') is the number of vocabularies belonging to a particular emotion among the vocabularies included in the collected contents (CNT).

According to the embodiment, the server 200 can infer emotion of the sentence about the predicate of the sentence.

When the server 200 deduces the emotion information of the paragraph, the emotion information of the sentence is inferred according to the probability model, and the emotion information of the paragraph can be inferred based on the emotion information of the inferred sentence.

Specifically, the server 200 can deduce sentence information of the paragraph according to the following Equation (3).

&Quot; (3) "

According to an embodiment, the server 200 extracts representative keywords in a paragraph using a page-rank algorithm, determines the number of keywords included in the sentence as a weight, multiplies the representative emotion of the sentence by the weight Values can be quantified for each emotion, and the emotion having the greatest value can be deduced as the representative emotion of the paragraph.

When the server 200 deduces the emotion information of the paragraph, the server 200 can deduce the emotion information of the entire document based on the emotion information of the inferred paragraph. The process of inferring the emotion information of the entire document based on the emotion information of the paragraph is the same as the process of inferring the emotion information of the paragraph based on the emotion information of the sentence, so the explanation is omitted.

The server 200 can deduce the representative emotion information (RSI) for the local information AI based on the representative emotion information for each of the collected contents CNT (S220). According to the embodiment, the server 200 analyzes the representative emotion information about the collected contents (CNT) and deduces the emotion class having the largest number of contents as the representative emotion information (RSI) for the local information (AI) .

While the present invention has been described with reference to exemplary embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the scope of the present invention . Accordingly, the true scope of the present invention should be determined by the technical idea of the appended claims.

10; Sensibility information based map providing system
100; terminal
200; server
GI: Map Information
AI: Local information
CNT: Contents
RSI: Representative emotion information
SMI: Emotional map information

Claims (8)

The server receiving map information from the terminal;
The server extracting local information from the map information;
The server collecting content related to the local information from a database;
The server analyzes each of the contents, maps each of the contents to an emotional class, which is a classification of human internal emotional states, based on the analysis result, and transmits the emotional class, which maps the largest number of contents, Inferring the representative emotion information to the representative emotion information; And
The server generates emotion map information including information related to the representative emotion information using the representative emotion information corresponding to the local information of the map information, and transmitting the emotion map information to the terminal ,
Wherein the emotional map information is representative emotional information corresponding to the local information, the emotional map information being displayed for each region on the map. The method according to claim 1,
Wherein the collecting of the contents comprises:
Wherein the server collects the contents marked with the local information. The method according to claim 1,
Wherein the collecting of the contents comprises:
Wherein the server collects contents including location information of a region corresponding to the local information. delete The server receiving map information from the terminal;
The server extracting local information from the map information;
The server collecting content related to the local information from a database;
The server analyzes each of the contents, maps each of the contents to an emotional class, which is a classification of human internal emotional states, based on the analysis result, and transmits the emotional class, which maps the largest number of contents, Inferring the representative emotion information to the representative emotion information; And
The server generates emotion map information including information related to the representative emotion information using the representative emotion information corresponding to the local information of the map information, and transmitting the emotion map information to the terminal ,
Wherein the emotion map information is classified and displayed on the map according to the representative emotion information. The server receiving map information from the terminal;
The server extracting local information from the map information;
The server collecting content related to the local information from a database;
The server analyzes each of the contents, maps each of the contents to an emotional class, which is a classification of human internal emotional states, based on the analysis result, and transmits the emotional class, which maps the largest number of contents, Inferring the representative emotion information to the representative emotion information;
Collecting content generated by a user of the terminal from at least one external database and inferring user emotion information for the user from the collected contents; And
The server generates emotion map information including information related to the representative emotion information using the representative emotion information corresponding to the local information of the map information, and transmitting the emotion map information to the terminal ,
Wherein the transmitting comprises:
Generating emotional map information based on the map information, the representative emotional information, and the user emotional information, and transmitting the emotional map information to the terminal. The method according to claim 6,
Wherein the emotion map information includes at least one of the local information corresponding to the representative emotion information associated with the user emotion information and the emotion path information linked with the local information corresponding to the representative emotion information related to the user emotion information A method of providing emotional information based map. A computer-readable recording medium having recorded thereon a computer program capable of performing the method of providing emotional information-based maps as set forth in any one of claims 1, 5,

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