Determination method and device for high-density city community user emotion grading prediction model, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a determination method and device for a high-density city community user emotion grading prediction model, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grid division of a geographic range where a high-density city community is located, and enabling each grid to be used for indicating a region in the high-density city community; performing high-density grading on the high-density urban communities according to the residential index information of the grids, and determining a high-density type region corresponding to each high-density grade; determining user emotion information, environment index information and residence index information in the high-density type region; and performing regression analysis on the residence index information and the environment index information according to the user emotion information, and establishing a user emotion grading prediction model corresponding to the high-density grade according to a regression analysis result. According to the scheme, the influence degree of the residence index information and the environment index information on the emotion of the user in the regions with different high-density levels in the high-density city community can be determined.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of public sentiment prediction in urban communities, and in particular to a method, device, electronic device and storage medium for determining a user sentiment grading prediction model in a high-density urban community. Background Art

[0002] The impact of various elements of traditional high-density urban communities on user emotions is usually measured through questionnaires, interviews, etc., which are limited by spatial and temporal scopes and are inefficient. At the same time, it is impossible to analyze user emotions in scenarios with different high-density levels. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a method, device, electronic device and storage medium for determining a user emotion grading prediction model for a high-density urban community, so as to at least partially solve the above-mentioned problems.

[0004] According to a first aspect of an embodiment of the present application, a method for determining a user emotion grading prediction model for a high-density urban community is provided, the method comprising:

[0005] Divide the geographical range of the high-density urban community into grids, each grid being used to indicate an area in the high-density urban community;

[0006] According to the residential index information of the grid, the high-density urban community is graded to a high-density level, and a high-density type area corresponding to each high-density level is determined;

[0007] Determining user emotion information, environmental index information, and residential index information in the high-density type area;

[0008] Based on the user emotion information, regression analysis is performed on the residential index information and the environmental index information, and a user emotion grading prediction model corresponding to the high-density level is established based on the regression analysis results. The user emotion grading prediction model is used to indicate the degree of influence of the residential index information and the environmental index information in the high-density type area corresponding to the high-density level on the emotion distribution information.

[0009] According to a second aspect of an embodiment of the present application, a device for determining a user emotion grading prediction model for a high-density urban community is provided, the device comprising:

[0010] A grid division module, used to divide the geographical range of the high-density urban community into grids, each grid being used to indicate an area in the high-density urban community;

[0011] A high-density classification module, used for high-density classification of the high-density urban community according to the residential index information of the grid, and determining the high-density type area corresponding to each high-density level;

[0012] A first determination module is used to determine user emotion information, environmental index information and residential index information in the high-density type area;

[0013] The second determination module is used to perform regression analysis on the residential index information and the environmental index information according to the user emotion information, and establish a user emotion grading prediction model corresponding to the high-density level according to the regression analysis results, and the user emotion grading prediction model is used to indicate the degree of influence of the residential index information and the environmental index information in the high-density type area corresponding to the high-density level on the emotion distribution information.

[0014] According to a third aspect of an embodiment of the present application, there is provided an electronic device comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to perform operations corresponding to the method for determining a user emotion grading prediction model for a high-density urban community as described in the first aspect.

[0015] According to a fourth aspect of an embodiment of the present application, a computer storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method for determining a user emotion grading prediction model for a high-density urban community as described in the first aspect is implemented.

[0016] According to the determination scheme of the user emotion grading prediction model for high-density urban communities provided in the embodiment of the present application, the geographical scope of the high-density urban community is divided into grids, so that the various indicator information in each grid area can be processed simultaneously, thereby improving the processing efficiency; at the same time, high-density grading is implemented for the high-density urban community according to the residential indicator information in the corresponding area of ​​each grid in the urban community, and the automatic division of the corresponding areas of different high-density grading is realized; the user emotion grading prediction model is determined by the emotion distribution information, residential indicator information and environmental indicator information in the high-density urban area under each high-density level, so that the degree of influence of the residential indicator information and the environmental indicator information on the emotion distribution information in the high-density type area can be accurately and efficiently determined based on the parameter information in the user emotion prediction model. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 A schematic diagram of an exemplary system for a method for determining a user emotion grading prediction model for a high-density urban community applicable to an embodiment of the present application;

[0019] Figure 2 It is a structural block diagram of a device for determining a user emotion grading prediction model for a high-density urban community according to an embodiment of the present application;

[0020] Figure 3 It is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the embodiments of the present application should fall within the scope of protection of the embodiments of the present application.

[0022] The specific implementation of the embodiment of the present application is further explained below in conjunction with the accompanying drawings of the embodiment of the present application.

[0023] Determination method for user sentiment rating prediction model in high-density urban communities

[0024] Step 101: Divide the geographical area where the high-density urban community is located into grids, where each grid is used to indicate an area in the high-density urban community.

[0025] In some embodiments, a grid area covering the entire geographical range can be created according to the geographical range of the high-density urban community, and the side length of each grid in the grid area can be configured to a preset length, such as a square grid with a side length of 1 km. Each grid in the grid area is used to indicate an area in the high-density urban community.

[0026] Step 102: high-density urban communities are graded according to the residential index information of the grid, and the high-density type area corresponding to each high-density level is determined.

[0027] In some embodiments, residential data related to residences in each grid of a high-density urban community may be obtained. The residential data may be obtained through a real estate trading platform such as Fangtianxia or a market research report, and may include information such as the name of the residence, the type of residence, the area of ​​the residence, the longitude and latitude coordinates of the residence, and the volume ratio of the residence.

[0028] Data cleaning operations can be performed on residential data, and data that lacks key architectural feature information such as house type and area can be directly deleted; for data that lacks non-critical information, missing values ​​can be replaced by sample data with similar architectural features. For example, if a sample lacks house color, data with similar residential type and floor area ratio can be found and replaced with its residential color.

[0029] In some embodiments, the residences in each grid can be determined based on the residence's longitude and latitude coordinates. The building density and floor area ratio of the grid can be determined based on the residence area and floor area ratio of the residences in each grid. The cumulative value of the residence area of ​​all residences in the grid can be determined as the floor area of ​​the residence in the grid, and the ratio between the floor area and the grid area can be determined as the building density; the average floor area ratio of all residences in the grid can be determined as the floor area ratio of the grid.

[0030] The building density and the volume ratio of each grid may be normalized respectively, the building density obtained after the normalization process is multiplied by the first weight, and the obtained product value and the product value obtained by multiplying the volume ratio obtained after the normalization process by the second weight are added to obtain the residential index information of the grid. For example, the first weight corresponding to the building density may be 0.5, and the second weight corresponding to the volume ratio may be 0.5.

[0031] In some embodiments, the high-density level corresponding to the residential index information of each grid can be determined based on the preset range of high-density levels. If there are five types of high-density levels, there are five preset ranges of different high-density levels, and the high-density level corresponding to the residential index information of each grid is determined based on these different ranges; after obtaining the high-density level corresponding to each grid, for all grids corresponding to each high-density level, the high-density type area corresponding to each high-density level is determined. The higher the high-density level corresponding to the high-density type area, the higher the value of the residential index information corresponding to the high-density area.

[0032] Step 103: Determine user emotion information, environmental index information, and residential index information in the high-density type area.

[0033] In some embodiments, comment data of all users in each high-density type area can be obtained. The comment data can be obtained through online comment websites of users in the urban community, community questionnaires, social media platforms such as Weibo, etc., and may include user number, comment time, comment location and comment content.

[0034] In some embodiments, the comment time of each user may be obtained based on the user number. If the time interval between two comment times of a user is less than a preset time threshold, the user is determined to be a resident user.

[0035] The comment data of each resident user can be obtained, and after the comment data is segmented into Chinese words, sentiment analysis is performed based on the sentiment dictionary to obtain the total sentiment score of the residents in each grid. If the total sentiment score is greater than 0, it is determined as a positive sentiment score, and if the total sentiment score is less than or equal to 0, it is determined as a negative sentiment score. Among them, the sentiment dictionary can use the Hownet sentiment dictionary, including 3730 Chinese positive evaluation words, 3116 Chinese negative evaluation words, 836 Chinese positive sentiment words, and 1254 Chinese negative sentiment words, supplemented with commonly used negative words such as "no", "no", "no" and other words and adverbs such as "very" and "slightly" to obtain a more comprehensive negative word library and adverb library. According to the comment location of the resident user, the grid location of the resident user can be determined according to the comment location; according to all the resident users in the grids in the high-density type area, the sum of the total emotion scores corresponding to each resident user in each grid in the high-density type area is determined as the total emotion score corresponding to all the resident users in the grid, and the total emotion distribution corresponding to each grid in the high-density area is determined according to the total emotion score corresponding to each grid; at the same time, according to the sum of the positive emotion scores of all the resident users in each grid in the high-density type area, the positive emotion distribution corresponding to each grid in the high-density type area can be determined; according to the total emotion score corresponding to the sum of the negative emotion scores of all the resident users in each grid in the high-density type area, the negative emotion distribution of the resident users corresponding to each grid in the high-density type area can be determined. Based on the visualization tool, the positive emotion distribution, negative emotion distribution and total emotion distribution of all grids corresponding to each high-density level can be displayed in the form of a map, and different colors can represent different emotion distribution types. Among them, after obtaining the total emotion score corresponding to all resident users in each grid in each high-density type area, the correlation between the residential indicator information and the total emotion score of each grid in each high-density type area can also be counted, such as determining the correlation between the building density and the total emotion score of each grid through the Pearson correlation analysis method, and determining the correlation between the residential volume ratio and the total emotion score of each grid, and then inferring the influence of residential indicator information on public emotions in residential areas with different high-density levels. As an example, if the high-density level is higher, the correlation between the building density and the total emotion score is lower, and the correlation between the residential volume ratio and the total emotion score is lower, then it is determined that the residential indicator information has a smaller influence on public emotions.

[0036] In some embodiments, the average total emotion scores of all resident users in the grids corresponding to each high-density type area can be obtained, and the average total emotion scores, positive emotion distribution, and negative emotion distribution corresponding to each high-density type area can be determined as user emotion information in each high-density type area.

[0037] In some embodiments, environmental indicator information in each grid can be obtained, and the environmental indicator information includes landscape environment data of urban communities, water road data of urban communities, and service facility data of urban communities. Among them, landscape environment data can be obtained through data disclosed by urban planning departments, geographic information systems, remote sensing satellite images, etc. Landscape environment data can include: park location, number of parks, location of scenic spots, and number of scenic spots; water road data can be obtained through data disclosed by traffic management departments, geographic information systems, remote sensing satellite images, urban planning blueprints, etc. Water road data can include: road distribution information and water distribution information. Road distribution information can include information such as the starting position, end position, and area occupied by the road, and water distribution information can include information such as the starting position, end position, and area occupied by the water; service facility data can be obtained through data disclosed by local governments, online map services, community surveys, etc. Service facility data can include information such as school location, number of schools, hospital location, number of hospitals, shopping mall location, number of shopping malls, cultural facility location, and number of cultural facilities.

[0038] For each network in the urban community, the number of landscape environments, the number of service facilities and the percentage of water roads in the grid can be determined; for each level of high-density type area, the landscape environment information is determined by the average value of the number of landscape environments in all grids corresponding to the high-density type area, the service facility quantity information is determined by the average value of the number of service facilities in all grids corresponding to the high-density type area, and the water road information is determined by the average value of the corresponding percentage of water roads in all grids corresponding to the high-density type area.

[0039] At least one of the landscape environment information, the service facility information and the water area road information may be determined as the environment index information.

[0040] Step 104: Perform regression analysis on the residential index information and the environmental index information according to the user emotion information, and establish a user emotion grading prediction model corresponding to the high-density level according to the regression analysis results.

[0041] In some embodiments, a user emotion grading prediction model for a high-density type area corresponding to the high-density level can be established based on the average value of the total emotion score in the user emotion information, the environmental index information, and the residential index information. The user emotion grading prediction model is shown in formula (1).

[0042] γ=β1x1+β2x2+…+β p x p +ε (1)

[0043] In formula (1), γ represents the average value of the total emotion score in the user emotion information corresponding to the high density level, β1, β2, βp The coefficient corresponding to at least one of the residential index information, landscape environment information in the environmental index information, service facility information, and water road information is represented. The coefficient also represents the degree of influence of the index information on the user's emotional information. ε represents the skewness corresponding to the user emotion prediction model. If the high-density urban community is classified into five high-density levels, the user emotion classification prediction models corresponding to the five high-density levels are determined based on the above method. Based on the coefficients in the user emotion classification prediction model corresponding to each high-density level, the degree of influence of the residential index information and environmental index information on the user's emotions at the high-density level is determined. The higher the coefficient corresponding to an index information, the higher the degree of influence of the index information on the user's emotions.

[0044] In some embodiments, after obtaining the user emotion grading prediction model corresponding to each high-density level, the indicator information that has a more obvious impact on the user's emotions can be determined according to the degree of influence of each indicator in each user emotion grading prediction model on the user's emotions; by improving the indicator information, the user emotion information output by the user emotion grading prediction model corresponding to the improved indicator information at the high-density level can be improved; and a corresponding urban planning scheme can be formulated based on the improved indicator information to improve the living satisfaction of urban community residents. As an example, if the distribution of negative emotions of users in the user emotion information in a high-density area is greater than the distribution of positive emotions, the coefficient corresponding to each indicator information can be determined based on the user emotion grading model corresponding to the high-density area, and the indicator information with a coefficient less than a preset threshold is determined as the indicator information that has a more obvious impact on the user's emotions.

[0045] Device for determining user emotion grading prediction model for high-density urban communities

[0046] Corresponding to the above-mentioned method embodiment for determining the user emotion grading prediction model for high-density urban communities, Figure 2 A schematic diagram of a device for determining a high-density urban community user emotion grading prediction model according to an embodiment of the present application is shown. Figure 2 As shown, the determination device 200 for the high-density urban community user emotion grading prediction model includes:

[0047] A grid division module 201 is used to divide the geographical range of the high-density urban community into grids, each grid being used to indicate an area in the high-density urban community;

[0048] A high-density classification module 202 is used to classify high-density urban communities according to the residential index information of the grid, and determine the high-density type area corresponding to each high-density level;

[0049] A first determination module 203, used to determine user emotion information, environmental index information and residential index information in a high-density type area;

[0050] The second determination module 204 is used to perform regression analysis on residential index information and environmental index information according to user emotional information, and establish a user emotion grading prediction model corresponding to the high-density level according to the regression analysis results. The user emotion grading prediction model is used to indicate the degree of influence of residential index information and environmental index information in the high-density type area corresponding to the high-density level on the emotion distribution information.

[0051] The device for determining the emotional grading prediction model for users in high-density urban communities of this embodiment is used to implement the corresponding method for determining the emotional grading prediction model for users in high-density urban communities in the aforementioned method embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here. In addition, the functional implementation of each module in the device for determining the emotional grading prediction model for users in high-density urban communities of this embodiment can refer to the description of the corresponding parts in the aforementioned method embodiment, which will not be repeated here.

[0052] Electronic devices

[0053] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the electronic device.

[0054] like Figure 3 As shown, the electronic device may include: a processor (processor) 302 , a communication interface (Communications Interface) 304 , a memory (memory) 306 , and a communication bus 308 .

[0055] in:

[0056] The processor 302 , the communication interface 304 , and the memory 306 communicate with each other via the communication bus 308 .

[0057] The communication interface 304 is used to communicate with other electronic devices or servers.

[0058] The processor 302 is used to execute the program 310, and specifically can execute the relevant steps in the above-mentioned embodiment of the method for determining the user emotion grading prediction model for a high-density urban community.

[0059] Specifically, the program 310 may include program codes, which include computer operation instructions.

[0060] The processor 302 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0061] The memory 306 is used to store the program 310. The memory 306 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0062] The program 310 may be specifically configured to enable the processor 302 to perform the following operations:

[0063] In an optional implementation, the program 310 is also used to enable the processor 302 to implement each step in the program 310. The specific implementation can refer to the corresponding steps and corresponding descriptions of the device in the above-mentioned method embodiment for determining the user emotion classification prediction model of a high-density urban community, and will not be repeated here. A person skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and module can refer to the corresponding process description in the above-mentioned method embodiment, and will not be repeated here.

[0064] An embodiment of the present application also provides a computer program product, including computer instructions, which instruct a computing device to perform operations corresponding to any method for determining a user emotion grading prediction model for a high-density urban community in the above-mentioned multiple method embodiments.

[0065] It should be pointed out that, according to the needs of implementation, the various components / steps described in the embodiments of the present application can be split into more components / steps, or two or more components / steps or partial operations of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present application.

[0066] The above-mentioned method according to the embodiment of the present application can be implemented in hardware, firmware, or implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or implemented as a computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the determination method for the high-density urban community user emotion rating prediction model described herein is implemented. In addition, when a general-purpose computer accesses a code for implementing the determination method for the high-density urban community user emotion rating prediction model shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the determination method for the high-density urban community user emotion rating prediction model shown herein.

[0067] Those of ordinary skill in the art will appreciate that the units and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present application.

[0068] The above implementation methods are only used to illustrate the embodiments of the present application, and are not limitations on the embodiments of the present application. Ordinary technicians in the relevant technical field can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application. Therefore, all equivalent technical solutions also belong to the scope of the embodiments of the present application. The scope of patent protection of the embodiments of the present application should be limited by the claims.

Claims

1. A method for determining a user emotion grading prediction model for a high-density urban community, characterized in that: The method comprises: Divide the geographical range of the high-density urban community into grids, each grid being used to indicate an area in the high-density urban community; According to the residential index information of the grid, the high-density urban community is graded to a high-density level, and a high-density type area corresponding to each high-density level is determined; Determining user emotion information, environmental index information, and residential index information in the high-density type area; Based on the user emotion information, regression analysis is performed on the residential index information and the environmental index information, and a user emotion grading prediction model corresponding to the high-density level is established based on the regression analysis results. The user emotion grading prediction model is used to indicate the degree of influence of the residential index information and the environmental index information in the high-density type area corresponding to the high-density level on the emotion distribution information.

2. The method according to claim 1, characterized in that The step of performing high-density grading on the high-density urban community according to the residential index information of the grid and determining the high-density type area corresponding to each high-density level includes: Determining the residences in the grid according to the latitude and longitude coordinates of the residences in the high-density urban community; Determining the building density and volume ratio of the grid according to the residential area and volume ratio of the residential buildings in the grid; The building density obtained after normalization is multiplied by the first weight, and then the product value obtained after normalization and the second weight is added to obtain the residential index information of the grid; Determining the high-density level corresponding to the residential indicator information based on a preset high-density level threshold range; All grids corresponding to each high-density level are determined to determine the high-density type area.

3. The method according to claim 2, characterized in that Determining the building density and the volume ratio of the grid according to the residential area and the residential volume ratio of the residential buildings in the grid includes: Determining the cumulative sum of the residential areas of all the residential buildings as the floor area of ​​the residential buildings in the grid; determining the ratio between the floor area and the grid area as the building density; The average value of the residential floor area ratios of all the residences in the grid is determined as the floor area ratio of the grid.

4. The method according to claim 1, characterized in that The determining the user emotion information in the high-density type area includes: Get review data for each resident user; Performing Chinese word segmentation and sentiment analysis on the comment data to obtain the total sentiment score of the resident user; If the total emotion score is greater than a preset threshold, it is determined as a positive emotion score; if the total emotion score is less than or equal to the preset threshold, it is determined as a negative emotion; The average value of the total emotion scores, the positive emotion distribution, and the negative emotion distribution of the resident users in all grids in the high-density type area are determined as the user emotion information in the high-density type area.

5. The method according to claim 4, characterized in that The obtaining of comment data of each resident user includes: Obtain the comment time of each user in the high-density urban community; If the time interval between the user's comments is less than a preset time threshold, the user is determined to be a resident user; Obtain comment data of the resident user.

6. The method according to claim 1, characterized in that Determining environmental indicator information in the high-density type area includes: Obtaining the number of landscape environments, the number of service facilities, and the percentage of water roads in all grids corresponding to the high-density type area; Determine the average value of the number of landscape environments in each grid corresponding to the high-density type area as landscape environment information, determine the average value of the number of service facilities in each grid corresponding to the high-density type area as service facility quantity information, and determine the average value of the water road in each grid corresponding to the high-density type area as water road information; At least one of the landscape environment information, the service facility information and the water road information is determined as the environmental indicator information.

7. A device for determining a user emotion grading prediction model for a high-density urban community, characterized in that: The device comprises: A grid division module, used to divide the geographical range of the high-density urban community into grids, each grid being used to indicate an area in the high-density urban community; A high-density classification module, used to perform high-density classification on the high-density urban community according to the residential index information of the grid, and determine the high-density type area corresponding to each high-density level; A first determination module is used to determine user emotion information, environmental index information and residential index information in the high-density type area; The second determination module is used to perform regression analysis on the residential index information and the environmental index information according to the user emotion information, and establish a user emotion grading prediction model corresponding to the high-density level according to the regression analysis results, and the user emotion grading prediction model is used to indicate the degree of influence of the residential index information and the environmental index information in the high-density type area corresponding to the high-density level on the emotion distribution information.

8. An electronic device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the method for determining a user emotion grading prediction model for a high-density urban community as described in any one of claims 1-6.

9. A computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for determining a user emotion grading prediction model for a high-density urban community as described in any one of claims 1 to 6.

10. A computer program product, comprising computer instructions, wherein the computer instructions instruct a computing device to perform operations corresponding to the method for determining a user emotion grading prediction model for a high-density urban community as described in any one of claims 1-6.

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