CN112860995B - Interaction method, device, client, server and storage medium - Google Patents
Interaction method, device, client, server and storage medium Download PDFInfo
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Abstract
The application discloses an interaction method, an interaction device, a client, a server and a server, relates to the technical field of artificial intelligence, and particularly relates to the field of natural language processing and deep learning. The specific implementation scheme is as follows: after a first search word is obtained in response to user operation executed on a search page, the first search word is sent to a server, so that the server generates a search word sequence according to the first search word and a plurality of second search words adopted by historical search, and further, a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence, so that the confidence level of a target emotion is obtained, after the confidence level of the target emotion is obtained from the server, under the condition that the confidence level of the target emotion is larger than a confidence level threshold, a guide animation corresponding to the target emotion is displayed on the search page. Thus, the states of the negative emotion and the like of the user are discovered early through the search words input by the user, so that the negative emotion of the user is guided.
Description
Technical Field
The application discloses an interaction method, an interaction device, a client and a storage medium, particularly relates to the technical field of natural language processing, and particularly relates to the field of deep learning and artificial intelligence.
Background
The physical conditions of the current society develop rapidly, but the mental world of human individuals faces crisis, and more young people go to depression and even light life. The search engine is used as a scene with higher daily access frequency of users, records a plurality of marks of struggling of the user's mind, gives the user humanization care in a specific scene under the condition of not infringing the privacy of the user to the maximum extent, and can also exert the social responsibility sense of the Internet platform.
Disclosure of Invention
The application provides an interaction method, device, equipment and storage medium.
According to an aspect of the present application, there is provided an interaction method, including:
Responding to user operation executed on the search page to obtain a first search word;
Sending the first search word to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, and a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence so as to obtain the confidence of a target emotion;
Obtaining the confidence level of the target emotion from the server;
and displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than a confidence coefficient threshold value.
According to another aspect of the present application, there is provided another interaction method, comprising:
acquiring a first search word sent by a client in response to user operation of a search page;
querying a history record of the client to obtain a plurality of second search words adopted by history searching;
generating a search word sequence according to the first search word and the plurality of second search words;
carrying out emotion recognition on the search word sequence by adopting a trained emotion recognition model so as to obtain the confidence level of the target emotion;
And sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than a confidence coefficient threshold value.
According to another aspect of the present application, there is provided an interaction device comprising:
The response module is used for responding to the user operation executed on the search page to obtain a first search word;
The sending module is used for sending the first search word to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, and a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence so as to obtain the confidence of a target emotion;
the acquisition module is used for acquiring the confidence coefficient of the target emotion from the server;
and the display module is used for displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than a confidence coefficient threshold value.
According to another aspect of the present application, there is provided another interactive apparatus comprising:
The acquisition module is used for acquiring a first search word sent by the client in response to user operation of the search page;
the query module is used for querying the history record of the client to obtain a plurality of second search words adopted by history search;
The generation module is used for generating a search word sequence according to the first search word and the plurality of second search words;
The recognition module is used for carrying out emotion recognition on the search word sequence by adopting a trained emotion recognition model so as to obtain the confidence level of the target emotion;
And the sending module is used for sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than a confidence coefficient threshold value.
According to another aspect of the present application, there is provided a client, including:
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 interaction method described in the embodiments of the above aspect.
According to another aspect of the present application, there is provided a server including:
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 interaction method described in the embodiment of the other aspect.
According to another aspect of the present application, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the interaction method described in the above embodiments.
According to another aspect of the application, a computer program product is provided, comprising a computer program which, when being executed by a processor, implements the interaction method described in the above embodiments.
One embodiment of the above application has the following advantages or benefits: and early discovering states such as negative emotion of the user through search words input by the user so as to guide the negative emotion of the user.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
The drawings are included to provide a better understanding of the present application and are not to be construed as limiting the application. Wherein:
FIG. 1 is a flow chart of an interaction method according to a first embodiment of the present application;
Fig. 2 is a flow chart of an interaction method according to a second embodiment of the present application;
fig. 3 is a schematic flow chart of an interaction method according to a third embodiment of the present application;
fig. 4 is a flow chart of an interaction method according to a fourth embodiment of the present application;
fig. 5 is a schematic flow chart of an interaction method according to a fifth embodiment of the present application;
fig. 6 is a schematic structural diagram of an interaction device according to a sixth embodiment of the present application;
Fig. 7 is a schematic structural diagram of an interaction device according to a seventh embodiment of the present application;
Fig. 8 is a block diagram of a client for implementing an interaction method of an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those 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 application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
The existing search engine can perform big data mining on massive search logs of users, establish user portraits according to service requirements, and apply mining results to specific occasions, such as personalized advertisement recommendation.
Most of the existing user behavior analysis based on data mining is to recommend advertisement, which is simply a best choice. However, as the internet is fully developed, it slowly merges into a social hectic state, and most of internet platforms are particularly lacking in humanitarian construction.
Therefore, the application provides an interaction method, which comprises the steps of carrying out semantic recognition and emotion analysis on search words input by a user in a search engine to determine the confidence coefficient of the target emotion of the current user, and displaying a guide picture corresponding to the target emotion on a search page when the confidence coefficient of the target emotion is determined to be greater than a confidence coefficient threshold value.
The following describes an interaction method, an interaction device, interaction equipment and interaction storage media according to an embodiment of the present application with reference to the accompanying drawings.
Fig. 1 is a flow chart of an interaction method according to a first embodiment of the present application.
The embodiment of the application is exemplified by the configuration of the interaction method in the interaction device, and the interaction device can be applied to any client so that the client can execute the interaction function.
The client may be a personal computer (Personal Computer, abbreviated as PC), a cloud device, a mobile device, and the mobile device may be a hardware device with various operating systems, such as a mobile phone, a tablet computer, a personal digital assistant, a wearable device, and a vehicle-mounted device.
As shown in fig. 1, the interaction method, executed by the client, may include the following steps:
step 101, responding to user operation performed on a search page, and obtaining a first search word.
The search page may be a search page of various search engines, which is not limited in the present application.
In order to facilitate distinguishing, the search word input by the user on the search page is named as a first search word; and naming the search word input by the user in the historical time as a second search word. Of course, other naming schemes are possible and are not limited herein.
In the embodiment of the application, after the user inputs the search word in the search page, the client responds to the input operation executed by the user in the search page, and the first search word input by the user can be obtained.
It should be noted that the user operation may be an operation of inputting a search term by a user through a voice input manner, an operation of inputting a search term by a user through a manual input manner, or the like, which is not limited herein.
Step 102, sending the first search term to a server.
The first search word is used for generating a search word sequence with a plurality of second search words adopted by the historical search.
In the embodiment of the application, the client side responds to the user operation input by the user on the search page, and after the first search word is obtained, the first search word can be sent to the server.
The server may also obtain, from the historical search behavior log, a plurality of second search terms employed in searching the search page over the user's historical time. The historical search behavior log stores search words input in a search page in the historical time of a user. The server may also query the history of the client to obtain a plurality of second search terms employed by the user's historical search.
After receiving the first search word sent by the client, the server can generate a search word sequence by the first search word and a plurality of second search words.
It should be noted that, when the search word sequence is generated according to the first search word and the plurality of second search words, the ordering of the first search word and the plurality of second search words is not limited, and may be ordered in any order.
Further, the server adopts a trained emotion recognition model to perform emotion recognition on the search word sequence so as to obtain the confidence level of the target emotion.
The emotion recognition model is obtained by training a large number of training samples, and can accurately recognize the emotion of the search word so as to obtain the confidence level of the target emotion. Wherein the training sample includes text describing the target emotion.
The training samples in the application can be search words containing emotion stored in a server, or can be search words containing emotion input by a user, and the like, and are not limited herein.
Step 103, obtaining the confidence of the target emotion from the server.
In the embodiment of the application, the server adopts the trained emotion recognition model to carry out emotion recognition on the search word sequence so as to obtain the confidence coefficient of the target emotion, and then the confidence coefficient of the target emotion can be sent to the client so that the client can obtain the confidence coefficient of the target emotion from the server.
And 104, displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than a confidence coefficient threshold value.
The confidence threshold is a preset confidence value.
As one possible scenario, the confidence threshold may be a confidence value set by the client in response to user operation.
It will be appreciated that the target emotions may be divided into a plurality of levels, different levels corresponding to different confidence thresholds, which the user may set according to his own needs.
As an example, assuming the target emotion is depression, depression may be classified into three levels, low, medium and high, with the different levels having corresponding confidence thresholds that the user may set according to his own needs.
In the embodiment of the application, after the client acquires the confidence coefficient of the target emotion from the server, the confidence coefficient of the target emotion is compared with a confidence coefficient threshold value, and under the condition that the confidence coefficient of the target emotion is determined to be greater than the confidence coefficient threshold value, the guide animation corresponding to the target emotion is displayed on the search page.
As an example, assuming that the target emotion is a negative emotion, if it is determined that the confidence level of the negative emotion is greater than the confidence level threshold, a guide screen corresponding to the negative emotion may be displayed on the search page.
In another possible case, after the client acquires the confidence coefficient of the target emotion from the server, comparing the confidence coefficient of the target emotion with a confidence coefficient threshold value, and if the confidence coefficient of the target emotion is determined to be smaller than or equal to the confidence coefficient threshold value, skipping the search page to a display page for displaying the search result according to the search word.
According to the interaction method, after a first search word is obtained in response to user operation executed on a search page, the first search word is sent to a server, so that the server generates a search word sequence according to the first search word and a plurality of second search words adopted by historical search, and further, a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence, so that the confidence level of a target emotion is obtained, and after the confidence level of the target emotion is obtained from the server, a guide animation corresponding to the target emotion is displayed on the search page under the condition that the confidence level of the target emotion is larger than a confidence level threshold value. Thus, the states of the negative emotion and the like of the user are discovered early through the search words input by the user, so that the negative emotion of the user is guided.
On the basis of the embodiment, after the search page displays the guide animation corresponding to the target emotion, the user can be guided to enter the emotion treatment communication page so as to intervene in the negative emotion and other states of the user. The following describes in detail with reference to fig. 2, and fig. 2 is a schematic flow chart of an interaction method according to a second embodiment of the present application.
As shown in fig. 2, the interaction method may include the steps of:
In step 201, a first search term is obtained in response to a user operation performed on a search page.
Step 202, sending a first search word to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, and a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence so as to obtain the confidence of the target emotion.
Step 203, obtain the confidence of the target emotion from the server.
And 204, displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than the confidence coefficient threshold value.
In the embodiment of the present application, the implementation process of step 201 to step 204 may refer to the implementation process of step 101 to step 104 in the above embodiment, and will not be described herein.
Step 205, a target control is displayed.
The target control is a control displayed on the client and used for the user to interact with the client. The target control may be an interesting animation small person, or may be a control displayed in other forms, which is not limited herein.
As an example, in the event that the confidence level of the target emotion is determined to be greater than the confidence threshold, a colored egg robot may be displayed on the search page that guides the user into the treatment communication page in a cartoon fun manner.
And step 206, responding to the triggering operation of the target control, and displaying a target page corresponding to the target emotion.
In the embodiment of the application, after the client displays the target control, the user can be prompted to click the target control in a text and/or voice mode so as to respond to the triggering operation of the user on the target control and display the target page corresponding to the target emotion.
In one possible scenario, the client may display a presentation area for presenting knowledge content of the target emotion on the target page in response to a trigger operation of the target control. Therefore, the user can recognize the emotion of the user, and the user can pay attention to physical and mental health.
As an example, assuming the target emotion is depression, a presentation area for presenting knowledge content of depression may be displayed on the target page. For example, manifestations of depression, simple treatment methods, etc. may be displayed in the display area.
In another possible case, the client may also display an interaction area for invoking a dialogue service corresponding to the target emotion on the target page in response to the triggering operation on the target control. Thereby enabling the user to communicate conversationally with the robot or the psychotherapist, etc., to alleviate the negative emotion of the user.
Optionally, an interaction area of the artificial intelligence dialogue service may be displayed on the target page, so that the dialogue service displayed on the interaction area by the user communicates with the intelligent robot.
Optionally, an interaction area of the manual dialogue service may be displayed on the target page, so that the manual dialogue service displayed on the interaction area by the user communicates with the person.
When the user communicates with the intelligent robot or the person, the user may communicate by inputting text, may communicate by voice, may communicate by video, and the like, which is not limited herein.
In another possible case, the client responds to the triggering operation of the target control, and a display area for displaying the knowledge content of the target emotion and an interaction area for calling the dialogue service corresponding to the target emotion can be displayed on the target page at the same time.
As an example, assuming that the target emotion is depression, an interactive area for calling a dialogue service for depression and a presentation area for presenting knowledge content of depression may also be displayed at the same time on the target page.
In the embodiment of the application, after the search page displays the guide animation corresponding to the target emotion, the target control can be displayed so as to respond to the triggering operation of the target control and display the target page corresponding to the target emotion. Therefore, the emotion of the user is relieved by communicating with the user on the target page.
In order to implement the above embodiment, the present application proposes another interaction method.
Fig. 3 is a flow chart of an interaction method according to a third embodiment of the present application.
As shown in fig. 3, the interaction method, executed by the server, may include the steps of:
step 301, obtaining a first search word sent by a client in response to a user operation of a search page.
The search page may be a search page of various search engines, which is not limited in the present application.
In the embodiment of the application, after the user inputs the search word in the search page, the client responds to the input operation executed by the user in the search page, and after the first search word input by the user is acquired, the client sends the first search word to the server, so that the server acquires the first search word sent by the client.
It should be noted that the user operation may be an operation of inputting a search term by a user through a voice input manner, an operation of inputting a search term by a user through a manual input manner, or the like, which is not limited herein.
Step 302, querying a history of the client to obtain a plurality of second search terms used in the history search.
In the embodiment of the application, the server can query the historical search behavior log of the client to obtain a plurality of second search words adopted in the historical search of the user from the historical search behavior log.
Step 303, generating a search word sequence according to the first search word and the plurality of second search words.
The search word sequence is a sequence obtained by sequencing a plurality of search words.
In the embodiment of the application, after the server acquires a plurality of search words adopted by the historical search of the user, a search word sequence can be generated according to the first search word and a plurality of second search words.
It should be noted that, when the search word sequence is generated according to the first search word and the plurality of second search words, the ordering of the first search word and the plurality of second search words is not limited, and may be ordered in any order.
And 304, performing emotion recognition on the search word sequence by adopting a trained emotion recognition model so as to obtain the confidence level of the target emotion.
The emotion recognition model is obtained by training a large number of training samples, and can accurately recognize the emotion of the search word so as to obtain the confidence level of the target emotion. Wherein the training sample includes text describing the target emotion.
The training samples in the application can be the text which is stored by the server and used for describing the target emotion, the text which is input by the user and used for describing the target emotion, and the like, and are not limited herein.
In the embodiment of the application, after a large number of training samples are adopted to train the emotion recognition model, the trained emotion recognition model can be adopted to carry out emotion recognition on the search word sequence so as to obtain the confidence of the target emotion. Thus, the accuracy of emotion recognition of the search word sequence by the emotion recognition model can be improved.
And 305, sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than a confidence coefficient threshold value.
The confidence threshold is a preset confidence value.
As one possible scenario, the confidence threshold may be a confidence value set by the client in response to user operation.
It will be appreciated that the target emotions may be divided into a plurality of levels, different levels corresponding to different confidence thresholds, which the user may set according to his own needs.
As an example, assuming the target emotion is depression, depression may be classified into three levels, low, medium and high, with the different levels having corresponding confidence thresholds that the user may set according to his own needs.
In the embodiment of the application, the server adopts the trained emotion recognition model to carry out emotion recognition on the search word sequence, and after obtaining the confidence coefficient of the target emotion, the server can send the confidence coefficient of the target emotion to the client. After receiving the confidence level of the target emotion, the client compares the confidence level with a confidence level threshold.
In one possible case, if the confidence coefficient of the target emotion is determined to be greater than the confidence coefficient threshold, displaying the guide animation corresponding to the target emotion on the search page.
In another possible case, if the confidence level of the target emotion is determined to be less than or equal to the confidence level threshold, the search page jumps to a display page displaying the search result according to the search word.
According to the interaction method, after a server obtains a first search word sent by a client in response to user operation of a search page, a history record of the client is queried to obtain a plurality of second search words adopted by history search, a search word sequence is generated according to the first search word and the plurality of second search words, a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence to obtain confidence level of a target emotion, the confidence level is sent to the client, and under the condition that the confidence level is larger than a confidence level threshold value, a guide animation corresponding to the target emotion is displayed on the search page by the client. . Thus, the states of the negative emotion and the like of the user are discovered early through the search words input by the user, so that the negative emotion of the user is guided.
In the above-described embodiment, it has been mentioned that the search word sequence is subjected to emotion recognition using an emotion recognition model to obtain the confidence of the target emotion. The following describes in detail with reference to fig. 4, and fig. 4 is a schematic flow chart of an interaction method according to a fourth embodiment of the present application.
As shown in fig. 4, the interaction method, executed by the server, may include the steps of:
step 401, inputting the search word sequence into an emotion recognition model, so as to extract semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model, and obtaining semantic features of the search word sequence.
The feature extraction layer is used for extracting semantic features of the search word sequence to obtain semantic features of the search word sequence.
In the embodiment of the application, when the trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence, a feature extraction layer of the emotion recognition model can be adopted first to carry out semantic feature extraction on the search word sequence so as to obtain semantic features of each search word in the search word sequence.
Step 402, classifying semantic features by using a classification layer of the emotion recognition model to obtain a confidence level belonging to the target emotion.
The classifying layer is used for classifying the semantic features extracted by the feature extracting layer and scoring each search word.
In the embodiment of the application, the semantic feature extraction layer of the emotion recognition model is adopted to extract the semantic features of the search word sequence, and after the semantic features of the search word sequence are obtained, the semantic features can be classified by the classification layer to determine the target emotion to which each search word in the search word sequence belongs, so that the confidence coefficient belonging to the target emotion is obtained.
It should be explained that, the feature extraction layer and the classification layer of the emotion recognition model are trained by using training samples, so that semantic features of the search word sequence can be accurately extracted, and confidence belonging to the target emotion can be obtained.
On the basis of the embodiment, the embodiment of the application provides an interaction method.
Fig. 5 is a flow chart of an interaction method provided in a fifth embodiment of the present application.
As shown in fig. 5, the interaction method may include the steps of:
in step 501, the client obtains a first search term in response to a user operation performed on a search page.
In step 502, the client sends a first search term to the server.
In step 503, the server queries the history of the client to obtain a plurality of second search terms used in the history search.
In step 504, the server generates a sequence of search terms based on the first search term and the plurality of second search terms.
In step 505, the server uses the trained emotion recognition model to perform emotion recognition on the search word sequence to obtain the confidence level of the target emotion.
In step 506, the server sends the confidence of the target emotion to the client.
And step 507, displaying the guide animation corresponding to the target emotion on the search page under the condition that the client determines that the confidence coefficient is greater than the confidence coefficient threshold value.
Step 508, the target control is displayed at the client.
Step 509, the client responds to the triggering operation of the user on the target control, and displays a target page corresponding to the target emotion.
It should be noted that, the specific implementation process of the steps 501 to 509 may refer to the implementation process of each step in the foregoing embodiment, which is not described herein again.
In order to achieve the above embodiments, an embodiment of the present application provides an interaction device.
Fig. 6 is a schematic structural diagram of an interaction device according to a sixth embodiment of the present application.
As shown in fig. 6, the interaction device 600 may include: a response module 610, a sending module 620, an obtaining module 630, and a presentation module 640.
The response module 610 is configured to obtain a first search term in response to a user operation performed on the search page.
A sending module 620, configured to send the first search term to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, and a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence so as to obtain the confidence of the target emotion.
An obtaining module 630, configured to obtain the confidence level of the target emotion from the server.
And the display module 640 is configured to display a guide animation corresponding to the target emotion on the search page if the confidence level of the target emotion is greater than the confidence level threshold.
In one possible case, the interaction device 600 may further include:
The first display module is used for displaying the target control;
And the second display module is used for responding to the triggering operation of the target control and displaying a target page corresponding to the target emotion.
In another possible case, the target page includes at least one of the following:
The display area is used for displaying knowledge content of the target emotion;
and the interaction area is used for calling the dialogue service corresponding to the target emotion.
In another possible case, the interaction device 600 may further include:
and the setting module is used for responding to the user operation to set the confidence threshold value.
It should be noted that the explanation of the embodiments of the interaction method in fig. 1 and fig. 2 is also applicable to the interaction device, and is not repeated here.
According to the interactive device provided by the embodiment of the application, after the first search word is obtained in response to the user operation executed on the search page, the first search word is sent to the server, so that the server generates a search word sequence according to the first search word and a plurality of second search words adopted by the history search, and further, a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence, so that the confidence level of the target emotion is obtained, and after the confidence level of the target emotion is obtained from the server, a guide animation corresponding to the target emotion is displayed on the search page under the condition that the confidence level of the target emotion is greater than a confidence level threshold value. Thus, the states of the negative emotion and the like of the user are discovered early through the search words input by the user, so that the negative emotion of the user is guided.
In order to implement the above embodiment, the present application proposes another interactive device.
Fig. 7 is a schematic structural diagram of an interaction device according to a seventh embodiment of the present application.
As shown in fig. 7, the interaction device 700 may include: the system comprises an acquisition module 710, a query module 720, a generation module 730, an identification module 740 and a sending module 750.
The obtaining module 710 is configured to obtain a first search term sent by the client in response to a user operation of the search page.
And the query module 720 is configured to query the history of the client to obtain a plurality of second search terms used in the history search.
A generating module 730, configured to generate a search word sequence according to the first search word and the plurality of second search words.
The recognition module 740 is configured to perform emotion recognition on the search word sequence by using the trained emotion recognition model, so as to obtain a confidence level of the target emotion.
And the sending module 750 is used for sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than the confidence coefficient threshold value.
The identification module 740 may also be configured to, in one possible scenario:
Inputting the search word sequence into an emotion recognition model, and extracting semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model to obtain semantic features of the search word sequence;
and classifying semantic features by adopting a classification layer of the emotion recognition model to obtain the confidence coefficient of the target emotion.
In another possible case, the emotion recognition model is obtained by training with a training sample; wherein the training sample includes text describing the target emotion.
It should be noted that the explanation of the embodiments of the interaction method in the foregoing fig. 3 and fig. 4 is also applicable to the interaction device, and will not be repeated here.
According to the interactive device provided by the embodiment of the application, after the server acquires the first search word sent by the client in response to the user operation of the search page, the history record of the client is queried to obtain a plurality of second search words adopted by history search, a search word sequence is generated according to the first search word and the plurality of second search words, a trained emotion recognition model is adopted to carry out emotion recognition on the search word sequence to obtain the confidence level of the target emotion, the confidence level is sent to the client, and the client displays a guide animation corresponding to the target emotion on the search page under the condition that the confidence level is larger than the confidence level threshold value. . Thus, the states of the negative emotion and the like of the user are discovered early through the search words input by the user, so that the negative emotion of the user is guided.
According to embodiments of the present application, the present application also provides a client, a server, a readable storage medium and a computer program product.
In order to achieve the above embodiments, the present application proposes a client, including:
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 interaction method of fig. 1 or fig. 2.
In order to achieve the above embodiments, the present application proposes a server including:
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 interaction method described in fig. 3 or fig. 4.
To implement the above embodiments, the present application proposes a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the interaction method of fig. 1 or fig. 2, or to perform the interaction method of fig. 3 or fig. 4.
To achieve the above embodiments, the present application proposes a computer program product comprising a computer program which, when executed by a processor, implements the interaction method of fig. 1 or 2, or implements the interaction method described in fig. 3 or 4.
Fig. 8 shows a schematic block diagram of an example client 800 that may be used to implement an embodiment of the application. Clients 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 client 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 applications described and/or claimed herein.
As shown in fig. 8, the apparatus 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 802 or a computer program loaded from a storage unit 808 into a RAM (Random Access Memory ) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An I/O (Input/Output) interface 805 is also connected to bus 804.
Various components in device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a CPU (Central Processing Unit ), a GPU (Graphic Processing Units, graphics processing unit), various specialized AI (ARTIFICIAL INTELLIGENCE ) computing chips, various computing units running machine learning model algorithms, DSPs (DIGITAL SIGNAL Processor ), and any suitable Processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as interactions. For example, in some embodiments, the interactions may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto device 800 via ROM 802 and/or communication unit 809. When a computer program is loaded into RAM803 and executed by computing unit 801, one or more steps of the interactions described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the interaction in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above can be implemented in digital electronic circuitry, integrated Circuit System, FPGA (Field Programmable GATE ARRAY ), ASIC (Application-SPECIFIC INTEGRATED Circuit, application-specific integrated Circuit), ASSP (Application SPECIFIC STANDARD Product, application-specific standard Product), SOC (System On Chip ), CPLD (Complex Programmable Logic Device, complex programmable logic device), 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.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, RAM, ROM, EPROM (ELECTRICALLY PROGRAMMABLE READ-Only-Memory, erasable programmable read-Only Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
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: LAN (Local Area Network ), WAN (Wide Area Network, wide area network), internet and blockchain networks.
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. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service ("Virtual PRIVATE SERVER" or simply "VPS") are overcome.
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, so long as the desired results of the technical solution disclosed in the present application can be achieved, and are not limited herein.
The above embodiments do not limit the scope of the present 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 should be included in the scope of the present application.
Claims (14)
1. An interaction method, comprising:
Responding to user operation executed on the search page to obtain a first search word;
Sending the first search word to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, inputting the search word sequence into a trained emotion recognition model, extracting semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model to obtain semantic features of the search word sequence, and classifying the semantic features by adopting a classification layer of the emotion recognition model to obtain confidence belonging to target emotion;
Obtaining the confidence level of the target emotion from the server;
setting a confidence threshold in response to a user operation;
and displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than a confidence coefficient threshold value.
2. The interaction method according to claim 1, wherein after the search page displays the guide animation corresponding to the target emotion, the interaction method further comprises:
Displaying a target control;
And responding to the triggering operation of the target control, and displaying a target page corresponding to the target emotion.
3. The interaction method of claim 2, wherein the target page includes at least one of the following:
the display area is used for displaying the knowledge content of the target emotion;
and the interaction area is used for calling the dialogue service corresponding to the target emotion.
4. An interaction method, comprising:
acquiring a first search word sent by a client in response to user operation of a search page;
querying a history record of the client to obtain a plurality of second search words adopted by history searching;
generating a search word sequence according to the first search word and the plurality of second search words;
Inputting the search word sequence into a trained emotion recognition model, extracting semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model to obtain semantic features of the search word sequence, and classifying the semantic features by adopting a classification layer of the emotion recognition model to obtain confidence belonging to target emotion;
And sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than a confidence coefficient threshold, wherein the confidence coefficient threshold is set by a user.
5. The interaction method of claim 4, wherein,
The emotion recognition model is obtained by training a training sample; wherein the training sample includes text describing the target emotion.
6. An interaction device, comprising:
The response module is used for responding to the user operation executed on the search page to obtain a first search word;
the sending module is used for sending the first search word to a server; the first search word is used for generating a search word sequence with a plurality of second search words adopted by historical search, inputting the search word sequence into a trained emotion recognition model, extracting semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model to obtain semantic features of the search word sequence, and classifying the semantic features by adopting a classification layer of the emotion recognition model to obtain confidence belonging to target emotion;
the acquisition module is used for acquiring the confidence coefficient of the target emotion from the server;
the setting module is used for responding to the user operation to set a confidence threshold;
and the display module is used for displaying the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient of the target emotion is larger than a confidence coefficient threshold value.
7. The interaction device of claim 6, wherein the device further comprises:
The first display module is used for displaying the target control;
And the second display module is used for responding to the triggering operation of the target control and displaying a target page corresponding to the target emotion.
8. The interactive device of claim 7, wherein the target page comprises at least one of:
the display area is used for displaying the knowledge content of the target emotion;
and the interaction area is used for calling the dialogue service corresponding to the target emotion.
9. An interaction device, comprising:
The acquisition module is used for acquiring a first search word sent by the client in response to user operation of the search page;
the query module is used for querying the history record of the client to obtain a plurality of second search words adopted by history search;
The generation module is used for generating a search word sequence according to the first search word and the plurality of second search words;
The recognition module is used for inputting the search word sequence into a trained emotion recognition model so as to extract semantic features of the search word sequence by adopting a feature extraction layer of the emotion recognition model to obtain semantic features of the search word sequence, and classifying the semantic features by adopting a classification layer of the emotion recognition model to obtain the confidence coefficient belonging to the target emotion;
And the sending module is used for sending the confidence coefficient to the client so that the client displays the guide animation corresponding to the target emotion on the search page under the condition that the confidence coefficient is larger than a confidence coefficient threshold value, wherein the confidence coefficient threshold value is set by a user.
10. The interactive apparatus of claim 9, wherein,
The emotion recognition model is obtained by training a training sample; wherein the training sample includes text describing the target emotion.
11. A client, 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 interaction method of any of claims 1-5.
12. A server, 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 interaction method of claim 4 or 5.
13. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the interaction method of any of claims 1-5.
14. A computer program product comprising a computer program which, when executed by a processor, implements the interaction method of any of claims 1-5.
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Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113572893B (en) * | 2021-07-13 | 2023-03-14 | 青岛海信移动通信技术股份有限公司 | Terminal device, emotion feedback method and storage medium |
| CN116661664A (en) * | 2022-02-17 | 2023-08-29 | 腾讯科技（深圳）有限公司 | Emotional information interaction method and related equipment |
| CN114912039B (en) * | 2022-04-19 | 2026-03-17 | 北京字节跳动网络技术有限公司 | A method, apparatus, device and medium for displaying search effects |
| CN114861057B (en) * | 2022-05-17 | 2023-05-30 | 北京百度网讯科技有限公司 | Resource sending method, training of recommendation model and device |
| CN115239858A (en) * | 2022-07-08 | 2022-10-25 | 北京百度网讯科技有限公司 | Control method and device of virtual image, electronic equipment and readable storage medium |
| TWI904061B (en) * | 2025-06-06 | 2025-11-01 | 爍雲科技股份有限公司 | Computer program product, server device, interactive platform, and automatically replying method capable of responding to users’ sentiments |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105224554A (en) * | 2014-06-11 | 2016-01-06 | 阿里巴巴集团控股有限公司 | Search word is recommended to carry out method, system, server and the intelligent terminal searched for |
| CN107506349A (en) * | 2017-08-04 | 2017-12-22 | 卓智网络科技有限公司 | A kind of user's negative emotions Forecasting Methodology and system based on network log |
| CN108763545A (en) * | 2018-05-31 | 2018-11-06 | 深圳市零度智控科技有限公司 | Negative emotions interference method, device and readable storage medium storing program for executing, terminal device |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103137043B (en) * | 2011-11-23 | 2015-07-15 | 财团法人资讯工业策进会 | Advertisement Playing System and Advertisement Playing Method Combined with Search Engine Service |
| CN104063418A (en) * | 2014-03-17 | 2014-09-24 | 百度在线网络技术（北京）有限公司 | Search Recommended Methods and Apparatus |
| CN105260416A (en) * | 2015-09-25 | 2016-01-20 | 百度在线网络技术（北京）有限公司 | Voice recognition based searching method and apparatus |
| CN106649409A (en) * | 2015-11-04 | 2017-05-10 | 陈包容 | Method and apparatus for displaying search result based on scene information |
| CN105975492A (en) * | 2016-04-26 | 2016-09-28 | 乐视控股（北京）有限公司 | Search term prompt method and device |
| CN106407287A (en) * | 2016-08-29 | 2017-02-15 | 宇龙计算机通信科技(深圳)有限公司 | Multimedia resource pushing method and system |
| CN107515928A (en) * | 2017-08-25 | 2017-12-26 | 百度在线网络技术（北京）有限公司 | A kind of method, apparatus, server, storage medium for judging assets price tendency |
| CN108280200B (en) * | 2018-01-29 | 2021-11-09 | 百度在线网络技术（北京）有限公司 | Method and device for pushing information |
| CN111816211B (en) * | 2019-04-09 | 2023-06-02 | Oppo广东移动通信有限公司 | Emotion recognition method and device, storage medium and electronic equipment |
| CN110377726B (en) * | 2019-06-05 | 2020-08-25 | 光控特斯联(上海)信息科技有限公司 | Method and device for realizing emotion recognition of natural language text through artificial intelligence |
| CN111651586B (en) * | 2020-05-29 | 2023-05-30 | 北京小米松果电子有限公司 | Rule template generation method, rule template generation device and rule template generation medium for text classification |
-
2021
- 2021-02-04 CN CN202110166690.8A patent/CN112860995B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105224554A (en) * | 2014-06-11 | 2016-01-06 | 阿里巴巴集团控股有限公司 | Search word is recommended to carry out method, system, server and the intelligent terminal searched for |
| CN107506349A (en) * | 2017-08-04 | 2017-12-22 | 卓智网络科技有限公司 | A kind of user's negative emotions Forecasting Methodology and system based on network log |
| CN108763545A (en) * | 2018-05-31 | 2018-11-06 | 深圳市零度智控科技有限公司 | Negative emotions interference method, device and readable storage medium storing program for executing, terminal device |
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