CN111523036B - Search behavior mining method and device and electronic equipment - Google Patents
Search behavior mining method and device and electronic equipment Download PDFInfo
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- 230000006399 behavior Effects 0.000 claims description 39
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Abstract
The application discloses a search behavior mining method, a search behavior mining device and electronic equipment, and relates to the technical field of intelligent search. The specific implementation scheme is as follows: acquiring first search information of a target user group in a target time window; matching the first search information of the target user group in a target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category; and determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information. The method and the device can improve the mining effect of the search behavior of the user.
Description
Technical Field
The present disclosure relates to the field of intelligent search technologies in computer technologies, and in particular, to a search behavior mining method, a device, and an electronic device.
Background
With the development of internet technology, users often acquire things that the users want to know through the internet, for example: the things the user wants to know are searched through the search information (Query). Furthermore, the current user portrait product can mine some information of the user through the search behavior of the user, and only basic attributes and interest attention of the user can be mined through the search behavior of the user, so that the mining effect on the search behavior of the user is poor.
Disclosure of Invention
The application provides a search behavior mining method, a search behavior mining device and electronic equipment, and aims to solve the problem that the mining effect on search behaviors of users is poor.
In a first aspect, the present application provides a search behavior mining method, applied to an electronic device, including:
acquiring first search information of a target user group in a target time window;
matching the first search information of the target user group in a target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category;
and determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information.
According to the method and the device, the emotion information of the target user group can be mined through the seed search information and the emotion category corresponding to the seed search information, so that the mining effect of the search behavior of the user is improved.
Optionally, before the obtaining the search information of the target user group in the target time window, the method further includes:
and clustering a plurality of second search information, and determining the at least one seed search information through a clustering result, wherein the second search information is the search information acquired by the electronic equipment before the target time window.
In this embodiment, at least one seed search information may be efficiently determined by clustering the search information.
Optionally, before the obtaining the first search information of the target user group in the target time window, the method further includes:
acquiring third search information of a plurality of users, wherein geographic position data of the plurality of users are represented in a target geographic area, and the third search information is the search information input by the plurality of users before the target time window;
and matching the third search information of the plurality of users with the at least one seed search information to determine the target user group, wherein the third search information of the users in the target user group contains search information matched with the seed search information.
In this embodiment, a user group matching the seed search information may be determined.
Optionally, before the obtaining the first search information of the target user group in the target time window, the method further includes:
classifying fourth search information of the target user group before the target time window;
screening the classified fourth search information to remove search information which is not related to emotion in the classified fourth search information;
and labeling the emotion category of the at least one seed search information based on the filtered fourth search information.
In this embodiment, the search information is filtered, so that the labeling efficiency can be improved.
Optionally, the determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information includes:
and determining the number of the search information matched with each seed search information according to the matching result, and acquiring the search number information of each emotion type according to the emotion type associated with each seed search information.
In this embodiment, the search quantity information of each emotion category can be obtained, thereby better reflecting the emotion of the user.
In a second aspect, the present application provides a search behavior mining apparatus, applied to an electronic device, including:
the first acquisition module is used for acquiring first search information of the target user group in a target time window;
the first matching module is used for matching the first search information of the target user group in the target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category;
and the determining module is used for determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information.
Optionally, the apparatus further includes:
and the clustering module is used for clustering a plurality of second search information, and determining the at least one seed search information through a clustering result, wherein the second search information is the search information acquired by the electronic equipment before the target time window.
Optionally, the apparatus further includes:
the second acquisition module is used for acquiring third search information of a plurality of users, wherein the geographic position data of the plurality of users are represented in a target geographic area, and the third search information is the search information input by the plurality of users before the target time window;
and the second matching module is used for matching the third search information of the plurality of users with the at least one seed search information to determine the target user group, wherein the third search information of the users in the target user group contains search information matched with the seed search information.
Optionally, the apparatus further includes:
the classification module is used for classifying fourth search information of the target user group before the target time window;
the screening module is used for screening the classified fourth search information to remove search information which is irrelevant to emotion in the classified fourth search information;
and the labeling module is used for labeling the emotion category of the at least one seed search information based on the screened fourth search information.
Optionally, the determining module is configured to determine, according to the matching result, the number of search information matched with each piece of seed search information, and obtain, according to the emotion category associated with each piece of seed search information, search number information of each emotion category.
In a third aspect, the present application provides an electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the search behavior mining method provided herein.
In a fifth aspect, the present application provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the search behavior mining method provided herein.
One embodiment of the above application has the following advantages or benefits:
acquiring first search information of a target user group in a target time window; matching the first search information of the target user group in a target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category; and determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information. Because the technical means of mining the emotion information of the target user group through the seed search information and the emotion categories corresponding to the seed search information is adopted, the technical problem that the mining effect on the search behavior of the user is relatively poor is solved, and the technical effect of improving the mining effect on the search behavior of the user is further achieved.
Other effects of the above alternative will be described below in connection with specific embodiments.
Drawings
The drawings are for better understanding of the present solution and do not constitute a limitation of the present application. Wherein:
FIG. 1 is a flow chart of a search behavior mining method provided herein;
FIG. 2 is a schematic illustration of one emotion information provided herein;
FIG. 3 is a schematic diagram of a search behavior mining method provided herein;
FIG. 4 is a block diagram of a search behavior mining apparatus provided herein;
FIG. 5 is a block diagram of another search behavior mining apparatus provided herein;
FIG. 6 is a block diagram of another search behavior mining apparatus provided herein;
FIG. 7 is a block diagram of another search behavior mining apparatus provided herein;
fig. 8 is a block diagram of an electronic device for implementing a search behavior mining method of an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present application to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Referring to fig. 1, fig. 1 is a flowchart of a search behavior mining method provided in the present application, where the method is applied to an electronic device, as shown in fig. 1, and includes the following steps:
step S101, obtaining first search information (Query) of the target user group in the target time window.
The target user group may be a user group whose search information has a certain feature, or may be a user group in a certain location area, or a user group in a certain location area, and whose search information has a certain feature. The user group includes a plurality of users, wherein the users in the group may be updated periodically or in real time, and of course, the user group is not limited thereto, and may be a plurality of users that are fixed.
The target time window may be a month, a week, a day, etc. time window.
The first search information may be search information input by a user in one or more search platforms within the target time window.
Step S102, matching the first search information of the target user group in the target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category.
The seed search information may be predetermined.
The seed search information may be representative or typical search information, search information having a relatively high frequency of occurrence among search information of the user, or the like.
The matching of the search information with the at least one seed search information may be, for example, fuzzy matching of the search information with the at least one seed search information: before matching, search information expansion is performed based on seed search information, and then fuzzy matching is performed on the search information of the target user group, so that the matching effect is improved. The expansion of the search information may be to semantically expand the seed search information to search information similar to the seed search information. Of course, the search information expansion is only a preferred embodiment, and the application is not limited thereto, for example: and the search information of the target user group can be directly matched with the seed search information without expansion, so that the search information with the text similarity and the semantic similarity higher than the specific threshold value with the seed search information can be determined.
That is, the search information in the present application matches the seed search information, which may be the search information matches the seed search information or matches the search information expanded by the seed search information. The match may be a similarity or semantic similarity or other specific similarity above a specific threshold.
The matching result may indicate the search information matching with the respective sub-search information among the search information of the target user group, or may indicate the number of search information matching with the respective sub-search information among the search information of the target user group.
The emotion category corresponding to each seed search information association may be preconfigured, and the application may be that different seed search information is associated with different emotion types, which is not limited, and there may be a case that a plurality of seed search information are associated with the same emotion type. For example: there may be multiple emotion types in the present application, each emotion type corresponding to one or more seed search information.
And step S103, determining emotion information of the target user group according to the matching result and emotion categories associated with each piece of seed search information.
The determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information may be that the emotion category associated with the seed search information is associated with the search information matched with the seed search information, so as to obtain the emotion category of each piece of search information, and further determine the emotion information of each user, or may be that the number of pieces of search information of each emotion category is counted, so as to obtain the emotion information of the target user group.
That is, the emotion information may be information indicating the emotion type of each user, the number of emotion categories, or the like.
According to the method and the device, the emotion information of the target user group can be mined through the seed search information and the emotion category corresponding to the seed search information, so that the mining effect of the search behavior of the user is improved.
Optionally, before the obtaining the search information of the target user group in the target time window, the method further includes:
and clustering a plurality of second search information, and determining the at least one seed search information through a clustering result, wherein the second search information is the search information acquired by the electronic equipment before the target time window.
The second search information may be historical search information recorded by the electronic device before the target time window.
The plurality of search information may be a plurality of search information acquired in advance, for example: and a large amount of search information input by a user is acquired through the search platform. The plurality of search information may or may not include the search information of the target user group.
The clustering may be to cluster the same, similar search information into one category.
The determining of the at least one seed search information according to the clustering result may be selecting one or more representative, typical or most frequently occurring search information in each cluster as the seed search information of the cluster. That is, one or more seed search information may be determined for each cluster.
In this embodiment, at least one seed search information may be efficiently determined by clustering the second search information.
It should be noted that, the present application is not limited to the method of obtaining seed search information by the above clustering, for example: at least one seed search information may also be manually preconfigured.
Optionally, before the obtaining the search information of the target user group in the target time window, the method further includes:
acquiring third search information of a plurality of users, wherein geographic position data of the plurality of users are represented in a target geographic area, and the third search information is the search information input by the plurality of users before the target time window;
and matching the third search information of the plurality of users with the at least one seed search information to determine the target user group, wherein the third search information of the users in the target user group contains search information matched with the seed search information.
The third search information may be the same or different from the second search information, or the third search information may be part of the second search information.
The target geographic area may be a geographic area corresponding to a scene applied in the application.
In this embodiment, the user group matching the seed search information may be identified, and the information effective for the management and supervision of the target user group may be also provided for the target location area.
The determining the target user group may be determining a specific user in the user group, or may be determining characteristic information of the user group.
Optionally, before the obtaining the first search information of the target user group in the target time window, the method further includes:
classifying fourth search information of the target user group before the target time window;
screening the classified fourth search information to remove search information which is not related to emotion in the classified search information;
and labeling the emotion category of the at least one seed search information based on the filtered fourth search information.
The fourth search information may be the same or different search information from the third search information, or the fourth search information may be part of the plurality of third search information.
The classifying the search information of the target user group before the target time window may be that the search information is screened by an interest classification model of the user portrait existing at present, and categories which obviously do not display emotion, such as entertainment, video, games, education training and the like, are deleted, so that the subsequent labeling efficiency is improved, seed search information is labeled based on the screened search information, and the emotion category corresponding to the search information is obtained.
The marking of the emotion type of the at least one seed search information based on the filtered search information may be marking the emotion type of the at least one seed search information by a neural network or a manual method.
In addition, the screened search information can comprise the seed search information or the search information matched with the seed search information, so that when the emotion type of at least one seed search information is marked based on the screened search information, marking can be carried out based on more reference when the emotion type is marked, and the accuracy of marking the emotion type is improved.
In this embodiment, the search information is filtered, so that the labeling efficiency can be improved.
Of course, the emotion categories of various sub-search information can be marked directly by a manual mode in the application.
Optionally, the determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information includes:
and determining the number of the search information matched with each seed search information according to the matching result, and acquiring the search number information of each emotion type according to the emotion type associated with each seed search information.
The obtaining the search quantity information of each emotion category according to the emotion category associated with each seed search information may be determining search information corresponding to each emotion category.
Of course, the search quantity information of each emotion category may also be directly determined.
In this embodiment, the search number information of each emotion category may be obtained, so as to perform statistical analysis on the search information of the target user group, thereby better reflecting the emotion of the user. For example: in this embodiment, emotion information as shown in fig. 2 may be acquired, and related quantity information of each emotion category may be presented in fig. 2.
Alternatively, the search information mining method provided in the present application may include three stages as shown in fig. 3, where the first stage is target user group identification, and the stages may mainly include: clustering search information, labeling seed search information and identifying a target user group by combining the seed search information; the second stage is emotion category mining, which may mainly include: classifying target user group search information, screening and labeling the search information, and carrying out emotion seed search information, wherein the emotion seed search information is seed search information after the emotion classification is marked by the index; a third stage, which may mainly include: search information neighbor expansion and user search information match emotion classification and user group emotion category statistical analysis.
In the application, search information of a target user group in a target time window is acquired; matching the search information with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category; and determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information. The emotion information of the target user group is mined through the seed search information and emotion categories corresponding to the seed search information, so that the mining effect on the search behavior of the user can be improved.
Referring to fig. 4, fig. 4 is a block diagram of a search behavior mining apparatus provided in the present application, the apparatus being applied to an electronic device, and as shown in fig. 4, a search behavior mining apparatus 400 includes:
a first obtaining module 401, configured to obtain first search information of a target user group in a target time window;
a first matching module 402, configured to match first search information of the target user group in a target time window with at least one piece of seed search information, to obtain a matching result, where each piece of seed search information is associated with a corresponding emotion category;
a determining module 403, configured to determine emotion information of the target user group according to the matching result and emotion category associated with each seed search information.
Optionally, as shown in fig. 5, the apparatus further includes:
and the clustering module 404 is configured to cluster a plurality of second search information, and determine the at least one seed search information according to a clustering result, where the second search information is the search information acquired by the electronic device before the target time window.
Optionally, as shown in fig. 6, the apparatus further includes:
a second obtaining module 405, configured to obtain third search information of a plurality of users, where geographic location data of the plurality of users is represented in a target geographic area, where the third search information is search information input by the plurality of users before the target time window;
and a second matching module 406, configured to match the third search information of the plurality of users with the at least one seed search information to determine the target user group, where search information matching the seed search information exists in the third search information of the users in the target user group.
Optionally, as shown in fig. 7, the apparatus further includes:
a classification module 407, configured to classify fourth search information of the target user group before the target time window;
a screening module 408, configured to screen the classified fourth search information to remove search information that is not related to emotion in the classified fourth search information;
and a labeling module 409, configured to label the emotion category of the at least one seed search information based on the filtered fourth search information.
Optionally, the determining module 401 is configured to determine, according to the matching result, the number of search information matched with each piece of seed search information, and obtain, according to the emotion category associated with each piece of seed search information, search number information of each emotion category.
The device provided in this embodiment can implement each process implemented in the method embodiment of the present application, and can achieve the same beneficial effects, so that repetition is avoided, and no further description is provided here.
According to embodiments of the present application, an electronic device and a readable storage medium are also provided.
As shown in fig. 8, a block diagram of an electronic device according to a search behavior mining method according to an embodiment of the present application is shown. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the application described and/or claimed herein.
As shown in fig. 8, the electronic device includes: one or more processors 801, memory 802, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the electronic device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In other embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple electronic devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 801 is illustrated in fig. 8.
Memory 802 is a non-transitory computer-readable storage medium provided herein. The memory stores instructions executable by the at least one processor to cause the at least one processor to perform the search behavior mining method provided herein. The non-transitory computer readable storage medium of the present application stores computer instructions for causing a computer to perform the search behavior mining method provided by the present application.
The memory 802 is used as a non-transitory computer readable storage medium, and may be used to store a non-transitory software program, a non-transitory computer executable program, and modules, such as program instructions/modules (e.g., the first acquisition module 401, the first matching module 402, and the determination module 403 shown in fig. 4) corresponding to the search behavior mining method in the embodiment of the present application. The processor 801 executes various functional applications of the server and data processing, i.e., implements the search behavior mining method in the above-described method embodiment, by running non-transitory software programs, instructions, and modules stored in the memory 802.
Memory 802 may include a storage program area that may store an operating system, at least one application program required for functionality, and a storage data area; the storage data area may store data created according to the use of the electronic device of the search behavior mining method, and the like. In addition, memory 802 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 802 may optionally include memory remotely located with respect to processor 801, which may be connected to the electronic device of the search behavior mining method via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the search behavior mining method may further include: an input device 803 and an output device 804. The processor 801, memory 802, input devices 803, and output devices 804 may be connected by a bus or other means, for example in fig. 8.
The input device 803 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic device of the search behavior mining method, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointer stick, one or more mouse buttons, a track ball, a joystick, etc. The output device 804 may include a display apparatus, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device may be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASIC (application specific integrated circuit), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
These computing programs (also referred to as programs, software applications, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object-oriented programming language, and/or in assembly/machine language. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, first search information of the target user group in the target time window is obtained; matching the first search information of the target user group in a target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category; and determining the emotion information of the target user group according to the matching result and the emotion category associated with each piece of seed search information. The emotion information of the target user group is mined through the seed search information and emotion categories corresponding to the seed search information, so that the mining effect on the search behavior of the user can be improved.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present application may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application are intended to be included within the scope of the present application.
Claims (10)
1. The search behavior mining method is applied to the electronic equipment and is characterized by comprising the following steps of:
acquiring first search information of a target user group in a target time window;
matching the first search information of the target user group in a target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category, the matching result represents the search information matched with each piece of seed search information in the first search information, or the matching result represents the number of search information matched with each piece of seed search information in the first search information, and the seed search information is representative or typical;
determining emotion information of the target user group according to the matching result and emotion categories associated with each seed search information, wherein the emotion information of the target user group comprises: quantity information corresponding to each emotion category;
before the first search information of the target user group in the target time window is obtained, the method further comprises:
classifying fourth search information of the target user group before the target time window;
screening the classified fourth search information to remove search information which is not related to emotion in the classified fourth search information;
and labeling the emotion category of the at least one seed search information based on the filtered fourth search information.
2. The method of claim 1, wherein prior to the obtaining the search information for the target group of users within the target time window, the method further comprises:
and clustering a plurality of second search information, and determining the at least one seed search information through a clustering result, wherein the second search information is the search information acquired by the electronic equipment before the target time window.
3. The method of claim 1, wherein prior to the obtaining the first search information for the target group of users within the target time window, the method further comprises:
acquiring third search information of a plurality of users, wherein geographic position data of the plurality of users are represented in a target geographic area, and the third search information is the search information input by the plurality of users before the target time window;
and matching the third search information of the plurality of users with the at least one seed search information to determine the target user group, wherein the third search information of the users in the target user group contains search information matched with the seed search information.
4. A method according to any one of claims 1 to 3, wherein said determining mood information for the target user group based on the mood categories associated with the matching result and each seed search information comprises:
and determining the number of the search information matched with each seed search information according to the matching result, and acquiring the search number information of each emotion type according to the emotion type associated with each seed search information.
5. A search behavior mining apparatus applied to an electronic device, comprising:
the first acquisition module is used for acquiring first search information of the target user group in a target time window;
the first matching module is used for matching the first search information of the target user group in the target time window with at least one piece of seed search information to obtain a matching result, wherein each piece of seed search information is associated with a corresponding emotion category, the matching result represents the search information matched with each piece of seed search information in the first search information, or the matching result represents the number of pieces of search information matched with each piece of seed search information in the first search information, and the seed search information is representative or typical;
the determining module is configured to determine, according to the matching result and the emotion category associated with each piece of seed search information, emotion information of the target user group, where the emotion information of the target user group includes: quantity information corresponding to each emotion category;
the apparatus further comprises:
the classification module is used for classifying fourth search information of the target user group before the target time window;
the screening module is used for screening the classified fourth search information to remove search information which is irrelevant to emotion in the classified fourth search information;
and the labeling module is used for labeling the emotion category of the at least one seed search information based on the screened fourth search information.
6. The apparatus of claim 5, wherein the apparatus further comprises:
and the clustering module is used for clustering a plurality of second search information, and determining the at least one seed search information through a clustering result, wherein the second search information is the search information acquired by the electronic equipment before the target time window.
7. The apparatus of claim 5, wherein the apparatus further comprises:
the second acquisition module is used for acquiring third search information of a plurality of users, wherein the geographic position data of the plurality of users are represented in a target geographic area, and the third search information is the search information input by the plurality of users before the target time window;
and the second matching module is used for matching the third search information of the plurality of users with the at least one seed search information to determine the target user group, wherein the third search information of the users in the target user group contains search information matched with the seed search information.
8. The apparatus according to any one of claims 5 to 7, wherein the determining module is configured to determine, according to the matching result, a number of search information that each seed search information matches, and obtain search number information of each emotion category according to emotion category associated with each seed search information.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-4.
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