Information Processing Method, Apparatus, Device, Readable Storage Medium, and Product

Abstract

Embodiments of the present disclosure provide an information processing method, an apparatus, a device, an electronic device, a computer-readable storage medium, a computer program product, and a computer program, the method including: acquiring comment data corresponding to at least one target media content having an associated relationship with a preset object; performing an extraction operation on the comment data to acquire at least one question-and-answer content in the comment data, the question content including a question content and at least one answer content to the question content, which is associated with the preset object; and aggregating and displaying the at least one question-and-answer content in an interface associated with the preset object.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the fields of computer and network communication technologies, and in particular, to an information processing method, apparatus, device, electronic device, computer-readable storage medium, computer program product, and computer program.

Background Art

[0002] With the improvement of the hardware performance of terminal devices and the continuous progress of artificial intelligence technology, the number of application programs (abbreviated as Applications, Apps) operating on terminal devices is increasing. In the process of using these APPs, in order for users to better understand the currently displayed content, a question-and-answer area is set on the display screen, and users can check the questions in the question-and-answer area by themselves, or start questions in the question-and-answer area according to their actual needs.

Summary of the Invention

Problems to be Solved by the Invention

[0003] Embodiments of the present disclosure provide an information processing method, apparatus, device, electronic device, computer-readable storage medium, computer program product, and computer program to solve the conventional technical problem that users need to trigger the entry of the content themselves for the question-and-answer content in the question-and-answer information and the content is scarce.

Means for Solving the Problems

[0004] In a first aspect, embodiments of the present disclosure provide an information processing method, the method comprising: obtaining comment data including text data and / or video data and / or audio data corresponding to at least one target media content having an association relationship with a preset object; Performing an extraction operation on the comment data to obtain at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content from among the comment data, Aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object.

[0005] In a second aspect, an embodiment of the present disclosure provides an information processing apparatus, the apparatus including An acquisition module for acquiring comment data including text data and / or video data and / or audio data corresponding to at least one target media content having an association with a preset object, An extraction module for performing an extraction operation on the comment data to obtain at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content from among the comment data, A display module for aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object.

[0006] In a third aspect, an embodiment of the present disclosure provides an electronic device including a memory and a processor, The memory stores computer-executable instructions, When the processor executes the computer-executable instructions stored in the memory, the Recording program Processor executes the information processing method described in the above first aspect and various possible designs of the first aspect.

[0007] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

[0008] In a fifth aspect, an embodiment of the present disclosure provides a computer program product including a computer program. When the computer program is executed by a processor, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

[0009] In a sixth aspect, an embodiment of the present disclosure provides a computer program. When the computer program is executed by a processor, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

Advantages of the Invention

[0010] With the information processing method, apparatus, device, readable storage medium, and product provided by this embodiment, the method first obtains at least one piece of comment data corresponding to target media content having a preset object and a relationship, performs an extraction operation on the comment data, obtains at least one question-and-answer content associated with the preset object from the comment data, and aggregates and displays the obtained at least one question-and-answer content on an interface associated with the preset object, so as to realize the extraction operation of the question-and-answer content from the comment data of the target media content.

Brief Description of the Drawings

[0011] The following briefly describes the drawings that need to be used in the description of the embodiments or the prior art in order to more clearly explain the embodiments of the present disclosure and the solutions in the prior art. Naturally, the drawings described below are some embodiments of the present disclosure, and those skilled in the art can conceive of other drawings based on these drawings without creative effort.

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Modes for Carrying Out the Invention

[0012] The following, in order to make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, with reference to the drawings related to the embodiments of the present disclosure, the technical solutions are clearly and completely described. Naturally, the described embodiments are only a part of the embodiments of the present disclosure, not all of its embodiments. Those skilled in the art can, without creative effort based on the embodiments in the present disclosure, obtain all other embodiments that fall within the protection scope of the present disclosure.

[0013] Currently, when presenting Q&A information using related technical solutions, the generation of Q&A information requires the user to trigger it himself. Generally, since there is little question and answer content that can be presented, the actual needs of the user cannot be met.

[0014] The present disclosure provides an information processing method, apparatus, device, readable storage medium, and product for question-and-answer content in question-and-answer information, to solve the above-mentioned conventional technical problem that a user needs to trigger the entry of the content by himself / herself and the content is scarce.

[0015] It should be noted that the information processing method, apparatus, device, readable storage medium, and product provided by the present disclosure can be applied to scenes of obtaining various question-and-answer contents.

[0016] In order to enable a user to better understand a preset object associated with media content, generally, a question-and-answer area is set at a corresponding position of the media content, and the user can ask questions by clicking on the question-and-answer area and perform a confirmation operation on the answers to existing questions. However, since there is little question-and-answer content in the question-and-answer area obtained by using the above method, the user cannot obtain the content he / she wants even if he / she clicks on it, resulting in a poor user experience.

[0017] In the process of solving the above technical problem, as a result of intensive research, the inventor found that media content associated with a preset object generally has a large amount of comment data, and there is usually question-and-answer content in the comment data that contributes to the user's understanding of the preset object. Therefore, in order to realize the expansion of the question-and-answer content in the question-and-answer area, it was discovered that the question-and-answer content in the comment data can be obtained and the question-and-answer content obtained from the comment data can be intensively displayed on an interface associated with the preset object.

[0018] FIG. 1 is a flowchart of an information processing method provided by Embodiment 1 of the present disclosure. As shown in FIG. 1, the method includes steps 101 to 103.

[0019] In step 101, comment data corresponding to at least one target media content is obtained, where the target media content is media content having an association relationship with a preset object, and the comment data includes text data and / or video data and / or audio data.

[0020] The execution entity of this embodiment is an information processing device, and the information processing device can be coupled to a server, and since the server can be communicatively connected to a database, the acquisition of comment data can be realized. The server can also be communicatively connected to the user's terminal device, so that data processing can be performed based on the man-machine interaction between the user and the terminal device.

[0021] In this embodiment, for the media content associated with the preset object, generally, there is a lot of comment data, and there is usually question-and-answer content in the comment data to contribute to the user's understanding of the preset object. Therefore, in order to realize the acquisition of question-and-answer content, first, comment data corresponding to at least one target media content can be obtained. Here, the target media content is media content having an association relationship with a preset object. Specifically, the preset object can be an object such as a restaurant, a tourist attraction, a museum, etc. The media content can be any one of media content such as video, article, audio, etc. The comment data corresponding to the target media content is the comment made by the user of the application program on the target media content, and the comment data can be displayed on the comment panel of the target media content.

[0022] Specifically, media content having an association with a preset object can be understood as including the following content. The target media content includes a label corresponding to the preset object, and / or the text content corresponding to the target media content includes fields associated with the preset object, and / or the comment data corresponding to the target media content includes fields associated with the preset object, and / or the image content corresponding to the target media content includes an image corresponding to the preset object. One preset object can correspond to multiple target media contents, and the types of the association relationships between the multiple target media contents and the preset object may be different from each other.

[0023] Taking actual applications as an example, the target media content can be short video content posted on short video software, and the preset object can be an Instagrammable restaurant of the user. The association relationship between the target media content and the preset object may be a relationship in which the label of the restaurant is included in the short video content, and the user can enter the interface related to the restaurant by triggering the label. Or, referring to the above example, the short video content may include text content for explaining the short video. The association relationship between the target media content and the preset object may be a relationship in which the name of the restaurant is included in the text content. Or, referring to the above example, when the comment data corresponding to the short video content includes at least one comment about the restaurant, it can be determined that the short video content has an association relationship with the restaurant. Or, referring to the above example, when at least one frame of the image in the short video content includes the restaurant, it can be determined that the short video content has an association relationship with the restaurant.

[0024] When actually applied, the determination of the association relationship between the target media content and the preset object can be realized in one or more of the above forms, and the present disclosure does not limit this.

[0025] Optionally, the comment data includes text data and / or video data and / or audio data. When the comment data is text data, data analysis can be directly performed on the text data to realize the acquisition operation of the Q&A content.

[0026] When the comment data is audio data, the audio data can be converted into corresponding text data, and then the Q&A content in the text data can be extracted.

[0027] When the comment data is video data, in order to realize the acquisition of the Q&A content, it is necessary to perform content understanding on the video data. Optionally, image processing can be performed on the image of each frame in the video data to extract the Q&A content. Or, the image frames in the video data can be sampled at the preset sampling rate, and image processing can be performed on the collected samples to realize the extraction of the Q&A content. Also, the extraction of the Q&A content can be realized based on the image data obtained by image processing and the audio content extracted from the video data.

[0028] In step 102, an extraction operation is performed on the comment data to obtain at least one Q&A content associated with the preset object in the comment data, and the Q&A content includes question content and at least one answer content for the question content.

[0029] In this embodiment, the comment data contains a large amount of data. Some of these data are associated with preset objects, and some are not related to the preset objects. Therefore, after obtaining the comment data, an extraction operation is performed on the comment data, and an acquisition operation needs to be performed on at least one question-and-answer content associated with the preset object in the comment data.

[0030] Here, the question-and-answer content includes question content and at least one answer content corresponding to the question content. Referring to the above example, when the question content is the content of whether the restaurant is crowded, the answer content corresponding to the question content may be the content of being crowded, the content of being crowded during peak hours, the content of being crowded at 7 pm, etc.

[0031] In step 103, the at least one question-and-answer content is intensively displayed on the interface associated with the preset object.

[0032] In this embodiment, after obtaining at least one question-and-answer content, since a part of the question-and-answer content may include the same or similar questions, in order to realize the optimization of the question-and-answer content, an aggregation operation is performed on at least one question-and-answer content, and the at least one question-and-answer content on which the aggregation operation has been performed can be intensively displayed on the related screen of the preset object.

[0033] Optionally, the related screen may be a question-and-answer screen. When the user views the target media content, the user can check the preset object corresponding to the target media content. A question-and-answer area may be set within the display screen of the preset object, and the user can trigger the question-and-answer area to view the related screen. At least one question-and-answer content is displayed within the related screen.

[0034] Optionally, when the comment data is video data and / or audio data, after extracting the question-and-answer content included in the video data and / or audio data, the video data and / or audio data is converted into a character format, and the question-and-answer content in the character format is aggregated and displayed together with the related screen of the preset object. Alternatively, after extracting the question-and-answer content included in the video data and / or audio data, the video data and / or audio data containing the question-and-answer content can be directly displayed within the related screen. Further, a character conversion icon is set within a preset area around the question-and-answer content in the format of the video data and / or audio data, and the user can convert the video data and / or audio data into the question-and-answer content in the character format for confirmation by triggering the character conversion icon.

[0035] Furthermore, based on Embodiment 1, step 101 includes: obtaining at least one target media content for which the number of at least one type of interaction data exceeds a preset comment number threshold and / or at least one target media content for which the playback number exceeds a preset playback number threshold; and obtaining the comment data of the at least one target media content.

[0036] In this embodiment, for a large number of conventional application software, since there are a large number of media contents, in order to realize accurate screening of Q&A contents, first, it is necessary to screen at least one target media content from the large number of media contents. It can be screened based on the number of interaction data of the target media content. Here, the number of interaction data includes, but is not limited to, the number of comments, the number of likes, the number of retweets, the number of favorites, etc. corresponding to the target media content. Specifically, at least one target media content whose at least one number of interaction data exceeds the preset comment number threshold and / or at least one target media content whose playback number exceeds the preset playback number threshold can be obtained, and the comment data corresponding to the at least one target media content can be obtained.

[0037] The information processing method provided by this embodiment obtains at least one comment data corresponding to a target media content having a relevant relationship with a preset object, performs an extraction operation on the comment data, obtains at least one Q&A content associated with the preset object from the comment data, and aggregates and displays the obtained at least one Q&A content on an interface associated with the preset object, so as to realize the extraction operation of Q&A content from the comment data of the target media content. Different from the technical solution in the related art where the user asks questions and answers them by himself / herself, by obtaining the Q&A content in the comment data, the acquisition efficiency of the Q&A content can be effectively improved. As a result, the user's understanding of the preset object is improved, convenience is brought to the user's use, and the user experience is improved.

[0038] Optionally, based on any of the above embodiments, step 102 is Inputting the comment data into a preset question-and-answer content extraction model to obtain at least one question-and-answer content associated with the preset object in the comment data, Here, the question-and-answer content extraction model is obtained by training a preset model to be trained using a sample question-and-answer content dataset. The sample question-and-answer content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data. The marking information is used to characterize whether the comment data contains question-and-answer content.

[0039] In this embodiment, one question-and-answer content extraction model is preset. Comment data is input into the question-and-answer content extraction model, and at least one question-and-answer content associated with the preset object can be obtained from the comment data. Specifically, to obtain the question-and-answer content extraction model, first, a preset sample question-and-answer content dataset can be obtained. The sample question-and-answer content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data. The marking information is used to characterize whether the comment data contains question-and-answer content. The preset model to be trained is trained using the sample question-and-answer content dataset until the model to be trained converges, and the question-and-answer content extraction model is obtained.

[0040] FIG. 2 is a flowchart of an information processing method provided by Embodiment 2 of the present disclosure. Based on Embodiment 1, step 102 includes steps 201 to 204.

[0041] In step 201, question data associated with the preset object is obtained from the comment data.

[0042] In step 202, question data in the comment data is screened according to at least one preset target topic, at least one piece of question data is obtained, and the target topic is a feature information topic corresponding to the preset object.

[0043] In step 203, answer data corresponding to the question data is obtained, and it is determined whether the answer data is associated with the preset target topic. If the result of the determination is that it is associated with the preset target topic, the answer data is determined as target answer data corresponding to the question data.

[0044] In step 204, the at least one piece of question data and at least one piece of target answer data corresponding to each piece of question data are determined as the at least one question-and-answer content.

[0045] In this embodiment, after obtaining the question-and-answer data, a data processing operation can be further performed on the question-and-answer data. Here, the question-and-answer data may be obtained by extracting comment data using the above question-and-answer content extraction model, or may be obtained by using any other means, and the present disclosure does not limit this.

[0046] Specifically, first, question data associated with the preset object in the comment data is obtained, and according to at least one preset target topic, the question data in the comment data is screened, and at least one piece of question data can be obtained. Here, the target topic is a feature information topic corresponding to the preset object. For example, the target topic may be feature information such as taste, location, opening time, number of people, etc.

[0047] In order to obtain valid response data, for the above-mentioned question data, the corresponding response data for the question data is obtained, and it is determined whether the response data is associated with a preset target topic. If the response data is associated with the preset target topic, the response data is determined as the target response data corresponding to the question data, and at least one question data and at least one target response data corresponding to each question data can be determined as the at least one question-and-answer content.

[0048] Furthermore, based on any of the above embodiments, in step 203, the step of determining whether the response data is associated with the preset target topic is determining whether the preset target topic is included in the response data, and / or determining whether the target field corresponding to the target topic is included in the response data to obtain a determination result.

[0049] In this embodiment, during the selection of response data, it is possible to determine whether the preset target topic is included in the response data. For example, if the target topic is the topic of "delicious" and the response data includes the topic of "delicious", it can be determined that the response data is the target response data.

[0050] Optionally, it is possible to determine whether the target field corresponding to the target topic is included in the response data to obtain a determination result. For example, if the target topic is the topic of "where", the target fields can be xx District, xx Street, xx Building, etc. If it is detected that the above target fields are included in the response data, it can be determined that the response data is the target response data.

[0051] The information processing method provided by this embodiment can obtain the question-and-answer content in the comment data by using the question-and-answer content extraction model, so that the user can obtain better-quality content from the question-and-answer content, and the user experience is improved.

[0052] FIG. 3 is a flowchart of the information processing method provided by Embodiment 3 of the present disclosure. Based on any of the above embodiments, step 103 includes steps 301 to 303.

[0053] In step 301, the similarity information between each question-and-answer content is calculated respectively.

[0054] In step 302, based on the similarity information, an aggregation operation is performed on the question-and-answer content whose similarity exceeds the preset similarity threshold to determine the target question-and-answer content.

[0055] In step 303, the target question-and-answer content is displayed on the interface associated with the preset object.

[0056] In this embodiment, since some of the question-and-answer content may include the same or similar questions, in order to realize the optimization of the question-and-answer content, an aggregation operation is performed on at least one question-and-answer content, and at least one question-and-answer content on which the aggregation operation is performed can be aggregately displayed on the related screen of the preset object.

[0057] Specifically, the similarity information between each question-and-answer content is calculated respectively. Based on the similarity information, at least one question-and-answer content whose similarity exceeds the preset similarity threshold is determined. An aggregation operation is performed on at least one question-and-answer content whose similarity exceeds the preset similarity threshold to obtain the target question-and-answer content, and the target question-and-answer content can be displayed on the interface associated with the preset object.

[0058] Optionally, after performing an aggregation operation on at least one Q&A content whose similarity exceeds a preset similarity threshold, one of the Q&A contents and the corresponding answer data can be displayed on a related screen. Further, a expansion button can be displayed within a preset range around the currently displayed Q&A content, and in response to a trigger operation of the expansion button by the user, a plurality of aggregated Q&A contents can be displayed. Optionally, the text content on the expansion button may be content such as "Expand" or "X people want to know", where X includes the number of aggregated Q&A contents.

[0059] Furthermore, based on any of the above embodiments, the method further includes: determining the number of Q&A contents whose similarity to the target Q&A content exceeds a preset threshold; updating the follow count corresponding to the target Q&A content according to the number of the Q&A contents and the number of trigger operations of the user on the follow button associated with the target Q&A content.

[0060] In this embodiment, since the follow counts are different for different Q&A contents, in order to realize the determination of the follow count, the number of Q&A contents whose similarity to the target Q&A content exceeds a preset threshold can be determined, and this number can characterize the number of users who want to know the Q&A content in the comment data.

[0061] Furthermore, a follow button can be displayed on the display screen. The user can follow the Q&A content by triggering the follow button. After the user triggers the follow button, if additional answer data is generated for the Q&A content afterwards, a reminder of the Q&A content can be sent to the user in the form of a private message.

[0062] Specifically, according to the number of Q&A contents and the number of user trigger operations on the follow buttons associated with the target Q&A contents, the follow count corresponding to the target Q&A contents can be updated.

[0063] After updating the follow count corresponding to the target Q&A contents, an ordering operation can be performed on the target Q&A contents according to the follow count, and the target Q&A contents with a high follow count, that is, the Q&A contents that more users are likely to want to know, can be placed at the top for the user to quickly know the relevant contents.

[0064] Furthermore, based on any of the above embodiments, after step 103, the method further includes: updating the interaction count of the Q&A content according to the interaction count of the Q&A content in the interface associated with the preset object and the interaction count of the corresponding comment data of the Q&A content in the target media content.

[0065] In this embodiment, in the process of a user viewing Q&A content, the user may perform an interaction operation on the Q&A content. Here, the interaction operation can be like, retweet, favorite, etc. Determine the interaction count of the Q&A content in the interface associated with the preset object and the interaction count of the Q&A content in the original comment data, and update the interaction count of the Q&A content according to the interaction count of the Q&A content in the interface associated with the preset object and the interaction count of the corresponding comment data of the Q&A content in the target media content.

[0066] FIG. 4 is a schematic diagram of a display screen provided according to an embodiment of the present disclosure. As shown in FIG. 4, a preset interaction icon 42 is set in an interface 41 associated with a preset object. A user can trigger an operation on the interaction icon 42 through a screen interaction to realize an update of the interaction count.

[0067] FIG. 5 is a schematic diagram of another display screen provided according to an embodiment of the present disclosure. As shown in FIG. 5, in response to a trigger operation on any question-and-answer content 51 by a user, it is possible to jump to a detailed interface 52 corresponding to the question-and-answer content. The detailed interface 52 includes question information 53 corresponding to the question-and-answer content and at least one answer information 54. In a preset area around each answer information 54, an interaction icon 55 corresponding to the answer information 54 is set. A user can realize an interaction operation on the answer information 54 by triggering the interaction icon 55.

[0068] Optionally, according to an interaction operation on the answer information by a user and the interaction count in the comment data corresponding to the answer information, an update operation can be performed on the interaction count of the answer information.

[0069] Furthermore, based on any of the above embodiments, the method further includes: responding to a deletion operation on comment data of target media content by a user, and further including a step of performing a deletion operation on question-and-answer content corresponding to the comment data.

[0070] In this embodiment, a user can perform a deletion operation on comment data according to actual needs. When a deletion of comment data by a user is detected, a deletion operation can be performed on the question-and-answer content in response to the deletion operation.

[0071] Specifically, when the deletion of the answer data in the comment data by the user is detected, a deletion operation can be performed on the corresponding answer data in the Q&A content. When the deletion of the question data in the comment data by the user is detected, the entire Q&A content can be deleted according to the deletion operation.

[0072] The information processing method provided by this embodiment further improves the user experience by aggregating and displaying Q&A content.

[0073] FIG. 6 is a schematic structural diagram of an information processing apparatus provided by Embodiment 4 of the present disclosure. As shown in FIG. 6, the information processing apparatus includes an acquisition module 61, an extraction module 62, and a display module 63. Among them, the acquisition module 61 is used to acquire comment data corresponding to at least one target media content, where the target media content is media content having an association relationship with a preset object, and the comment data includes text data and / or video data and / or audio data. The extraction module 62 performs an extraction operation on the comment data and is used to acquire at least one Q&A content associated with the preset object in the comment data, where the Q&A content includes question content and at least one answer content for the question content. The display module 63 is used to aggregately display the at least one Q&A content on an interface associated with the preset object.

[0074] Furthermore, based on Embodiment 4, the acquisition module 61 is to acquire at least one target media content with at least one interaction data quantity exceeding a preset comment quantity threshold and / or at least one target media content with a playback quantity exceeding a preset playback quantity threshold, It is used to obtain the comment data of the at least one target media content.

[0075] Furthermore, based on any of the above embodiments, the target media content includes a label corresponding to the preset object, and / or the text content corresponding to the target media content includes a field associated with the preset object, and / or the comment data corresponding to the target media content includes a field associated with the preset object, and / or the image content corresponding to the target media content includes an image corresponding to the preset object.

[0076] Furthermore, based on any of the above embodiments, the extraction module 62 is to obtain the question data associated with the preset object from the comment data, to select the question data in the comment data according to at least one target topic of the preset, and obtain at least one question data, where the target topic is a feature information topic corresponding to the preset object, to obtain the answer data corresponding to the question data, determine whether the answer data is associated with the target topic of the preset, and if the result of the determination is associated with the target topic of the preset, determine the answer data as the target answer data corresponding to the question data, It is used to determine the at least one question data and the at least one target answer data corresponding to each question data as the at least one question-and-answer content.

[0077] Furthermore, based on any of the above embodiments, the extraction module 62 is It is used to determine whether the preset target topic is included in the answer data, and / or to determine whether the target field corresponding to the target topic is included in the answer data, and obtain a determination result.

[0078] Furthermore, based on any of the above embodiments, the extraction module 62 is used to input the comment data into a preset Q&A content extraction model to obtain at least one Q&A content associated with the preset object in the comment data. Here, the Q&A content extraction model is obtained by training a preset model to be trained using a sample Q&A content dataset. The sample Q&A content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data. The marking information is used to characterize whether the comment data includes Q&A content.

[0079] Furthermore, based on any of the above embodiments, the display module 63 is used to calculate the similarity information between each Q&A content respectively, perform an aggregation operation on the Q&A content whose similarity exceeds a preset similarity threshold based on the similarity information, and determine the target Q&A content, and display the target Q&A content on an interface associated with the preset object.

[0080] Furthermore, based on any of the above embodiments, the apparatus includes a determination module for determining the number of Q&A contents whose similarity with the target Q&A content exceeds a preset threshold. An update module for updating the number of follows corresponding to the target Q&A content according to the number of the Q&A contents and the number of trigger operations of the user on the follow button associated with the target Q&A content.

[0081] Furthermore, based on any of the above embodiments, the apparatus Further includes an update module further used to update the number of interactions of the Q&A content according to the number of interactions of the Q&A content in the interface associated with the preset object and the number of interactions of the corresponding comment data of the Q&A content in the target media content.

[0082] Furthermore, based on any of the above embodiments, the apparatus Further includes a deletion module for performing a deletion operation on the Q&A content corresponding to the comment data in response to a deletion operation of the comment data of the target media content by the user.

[0083] Another embodiment of the present disclosure further provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the information processing method described in any of the above embodiments is realized.

[0084] Another embodiment of the present disclosure further provides a computer program product including a computer program, and when the computer program is executed by a processor, the information processing method described in any of the above embodiments is realized.

[0085] The device provided by this embodiment can be used to execute the technical solution according to the method embodiment above. Since its implementation principle and technical effect are similar, this embodiment will not be described repeatedly here.

[0086] To implement the above embodiments, embodiments of the present disclosure further provide an electronic device.

[0087] Another embodiment of the present disclosure further provides an electronic device including a processor and a memory, wherein the memory stores computer-executable instructions, and when the processor executes the computer-executable instructions stored in the memory, the processor executes the information processing method described in any of the above embodiments.

[0088] FIG. 7 is a schematic structural diagram of an electronic device provided by Embodiment 5 of the present disclosure. Referring to FIG. 7, a schematic structural diagram of an electronic device 700 suitable for implementing the embodiments of the present disclosure is shown. The electronic device 700 can be a terminal device or a server. The terminal device can include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, personal digital assistants (abbreviated as PDAs), tablet computers (abbreviated as PADs), portable media players (abbreviated as PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. The electronic device shown in FIG. 7 is merely an example and should not impose any limitations on the functions and usage scope of the embodiments of the present disclosure.

[0089] As shown in FIG. 7, the electronic device 700 can include a processing device (such as a central processing unit or a graphics processor) 701, and the processing device 701 can execute various appropriate operations and processes according to a program stored in a read-only memory (abbreviated as ROM) 702 or a program loaded from a storage device 708 into a random access memory (abbreviated as RAM) 703. Various programs and data necessary for the operation of the electronic device 700 are also stored in the RAM 703. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (abbreviated as I / O) interface 705 is also connected to the bus 704.

[0090] Typically, an input device 706 including a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc., an output device 707 including a liquid crystal display (abbreviated as LCD), a speaker, a vibrator, etc., a storage device 708 including a magnetic tape, a hard disk, etc., and a communication device 709 can be connected to the I / O interface 705. The communication device 709 can enable the electronic device 700 to communicate with other devices wirelessly or wiredly to exchange data. FIG. 7 shows an electronic device 700 equipped with various devices, but it should be understood that not all of the illustrated devices need to be implemented or arranged. Alternatively, more or fewer devices can be implemented or arranged.

[0091] In particular, according to the embodiments of the present disclosure, the above-described process described with reference to the flowchart can be implemented as a computer software program. For example, embodiments of the present disclosure include a computer program product including a computer program stored on a computer-readable medium, the computer program including program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processing device 701, the above-described functions limited by the method according to the embodiments of the present disclosure are executed.

[0092] Note that the computer-readable medium described in this disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (abbreviated as Erasable Programmable Read Only Memory, EPROM or flash memory), optical fibers, portable compact disk read-only memory (abbreviated as Compact Disk Read Only Memory, CD-ROM), optical storage devices, magnetic memory components, or any suitable combination thereof. In this disclosure, the computer-readable storage medium can be any tangible medium that includes or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device. In this disclosure, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take many forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can further be any computer-readable medium other than the computer-readable storage medium, and the computer-readable signal medium can transmit, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code included in the computer-readable medium can be transmitted using any suitable medium, including, but not limited to, wires, optical fiber cables, radio frequency (RF), or any suitable combination thereof.

[0093] The above computer-readable medium may be included in the above electronic device or may exist independently without being assembled into the electronic device.

[0094] The above computer-readable medium has one or more programs installed thereon. When the above one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiments.

[0095] The computer program code for executing the operations of the present disclosure can be written in one or more programming languages including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as the "C" language or similar programming languages, or combinations thereof. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer via any type of network such as a Local Area Network (LAN) or a Wide Area Network (WAN), or can also be connected to an external computer (for example, connected via the Internet using an Internet service provider).

[0096] The flowcharts and block diagrams in the drawings illustrate the architecture, functionality, and operation that can be implemented by systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions shown in the blocks can be executed in an order different from that shown in the figures. For example, two blocks shown connected and displayed can actually be executed substantially in parallel, or, depending on the related functions, the blocks may be executed in reverse order. Additionally, each block of the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system for performing the specified function or operation, or can also be implemented using a combination of dedicated hardware and computer instructions.

[0097] The units described in the embodiments of the present disclosure can be implemented in software or in hardware. The name of a unit may not be intended to limit the unit itself in a specific situation. For example, the first acquisition unit may be described as "a unit for acquiring at least two Internet protocol addresses".

[0098] The functions described above in this specification can be executed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used include, but are not limited to, Field Programmable Gate Array (FPGA), Application Specific Integrated Circuit (ASIC), Application Specific Standard Product (ASSP), System on chip (SOC), Complex Programmable Logic Device (CPLD), etc.

[0099] In the context of the present disclosure, a machine-readable medium can be a tangible medium that includes or stores a program that can be used by or in combination with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of machine-readable storage media can include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic memory components, or any suitable combination of the above.

[0100] In a first aspect, according to one or more embodiments of the present disclosure, an information processing method is provided, and the method includes Obtaining comment data corresponding to at least one target media content having an association relationship with a preset object; Performing an extraction operation on the comment data including text data and / or video data and / or audio data, and obtaining at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content from the comment data; Aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object.

[0101] According to one or more embodiments of the present disclosure, the step of obtaining comment data corresponding to at least one target media content includes: obtaining at least one target media content with at least one interaction data number exceeding a threshold number of preset comments and / or at least one target media content with a playback number exceeding a threshold number of preset playbacks; and obtaining comment data of the at least one target media content.

[0102] According to one or more embodiments of the present disclosure, the target media content includes a label corresponding to the preset object, and / or the text content corresponding to the target media content includes a field associated with the preset object, and / or the comment data corresponding to the target media content includes a field associated with the preset object, and / or the image content corresponding to the target media content includes an image corresponding to the preset object.

[0103] According to one or more embodiments of the present disclosure, the step of performing an extraction operation on the comment data to obtain at least one question-and-answer content associated with the preset object in the comment data includes: obtaining question data associated with the preset object in the comment data; screening the question data in the comment data according to at least one target topic of the preset, which is a feature information topic corresponding to the preset object, to obtain at least one question data; obtaining answer data corresponding to the question data, determining whether the answer data is associated with the target topic of the preset, and if the result of the determination is that it is associated with the target topic of the preset, determining the answer data as target answer data corresponding to the question data; and determining the at least one question data and at least one target answer data corresponding to each question data as the at least one question-and-answer content.

[0104] According to one or more embodiments of the present disclosure, the step of determining whether the answer data is associated with the target topic of the preset includes: determining whether the preset target topic is included in the answer data, and / or determining whether the target field corresponding to the target topic is included in the answer data to obtain a determination result.

[0105] According to one or more embodiments of the present disclosure, the step of performing an extraction operation on the comment data and obtaining at least one question-and-answer content associated with the preset object in the comment data includes inputting the comment data into a preset question-and-answer content extraction model to obtain at least one question-and-answer content associated with the preset object in the comment data. The question-and-answer content extraction model is obtained by training a preset model to be trained using a sample question-and-answer content dataset. The sample question-and-answer content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data. The marking information is used to characterize whether the comment data includes question-and-answer content.

[0106] According to one or more embodiments of the present disclosure, the step of aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object includes calculating similarity information between each pair of question-and-answer contents, performing an aggregation operation on the question-and-answer contents whose similarity exceeds a preset similarity threshold based on the similarity information to determine target question-and-answer contents, and displaying the target question-and-answer contents on an interface associated with the preset object.

[0107] According to one or more embodiments of the present disclosure, the method further includes determining the number of question-and-answer contents whose similarity with the target question-and-answer contents exceeds a preset threshold, and updating the number of followers corresponding to the target question-and-answer contents according to the number of the question-and-answer contents and the number of user trigger operations on the follow button associated with the target question-and-answer contents.

[0108] According to one or more embodiments of the present disclosure, after the step of aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object, the method further includes updating the interaction count of the question-and-answer content according to the interaction count of the question-and-answer content in the interface associated with the preset object and the interaction count of the corresponding comment data of the question-and-answer content in the target media content.

[0109] According to one or more embodiments of the present disclosure, the method further includes, in response to a deletion operation of comment data of target media content by a user, performing a deletion operation on the question-and-answer content corresponding to the comment data.

[0110] In a second aspect, according to one or more embodiments of the present disclosure, an information processing apparatus is provided, the apparatus including: an acquisition module for acquiring comment data including text data and / or video data and / or audio data corresponding to at least one target media content having an association relationship with a preset object; 61 and an extraction module for performing an extraction operation on the comment data to obtain at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content in the comment data; 62 and a display module for aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object. 63 and includes.

[0111] According to one or more embodiments of the present disclosure, the acquisition module is used for acquiring at least one target media content in which the number of at least one interaction data exceeds a preset comment number threshold and / or at least one target media content in which the number of reproductions exceeds a preset reproduction number threshold, and for acquiring comment data of the at least one target media content.

[0112] According to one or more embodiments of the present disclosure, the target media content includes a label corresponding to the preset object, and / or the text content corresponding to the target media content includes a field associated with the preset object, and / or the comment data corresponding to the target media content includes a field associated with the preset object, and / or the image content corresponding to the target media content includes an image corresponding to the preset object.

[0113] According to one or more embodiments of the present disclosure, the extraction module 62 is used for acquiring question data associated with the preset object from the comment data, selecting question data in the comment data according to at least one target topic of the preset, which is a feature information topic corresponding to the preset object, to obtain at least one question data, acquiring answer data corresponding to the question data, determining whether the answer data is associated with the preset target topic, and if the result of the determination is that it is associated with the preset target topic, determining the answer data as target answer data corresponding to the question data, and determining the at least one question data and at least one target answer data corresponding to each question data as the at least one question-and-answer content.

[0114] According to one or more embodiments of the present disclosure, the extraction module 62 is used to determine whether the preset target topic is included in the answer data and / or whether the target field corresponding to the target topic is included in the answer data, and obtain a determination result.

[0115] According to one or more embodiments of the present disclosure, the extraction module 62 is used to input the comment data into a preset question-and-answer content extraction model, and obtain at least one question-and-answer content associated with the preset object in the comment data. The question-and-answer content extraction model is obtained by training a preset model to be trained using a sample question-and-answer content dataset. The sample question-and-answer content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data. The marking information is used to characterize whether the comment data includes question-and-answer content.

[0116] According to one or more embodiments of the present disclosure, the display module 63 is used to calculate similarity information between each question-and-answer content respectively, perform an aggregation operation on the question-and-answer content whose similarity exceeds a preset similarity threshold based on the similarity information, determine target question-and-answer content, and display the target question-and-answer content on an interface associated with the preset object.

[0117] According to one or more embodiments of the present disclosure, the apparatus further includes a determination module for determining the number of question-and-answer contents whose similarity to the target question-and-answer content exceeds a preset threshold, and an update module for updating the follow number corresponding to the target question-and-answer content according to the number of the question-and-answer contents and the number of user trigger operations on the follow button associated with the target question-and-answer content.

[0118] According to one or more embodiments of the present disclosure, the apparatus further includes an update module for updating the interaction number of the question-and-answer content according to the interaction number of the question-and-answer content in the interface associated with the preset object and the interaction number of the corresponding comment data of the question-and-answer content in the target media content.

[0119] According to one or more embodiments of the present disclosure, the apparatus further includes a deletion module for performing a deletion operation on the question-and-answer content corresponding to the comment data in response to a deletion operation of the comment data of the target media content by the user.

[0120] In a third aspect, according to one or more embodiments of the present disclosure, there is provided an electronic device including at least one processor and a memory, wherein the memory stores computer-executable instructions, when the at least one processor executes the computer-executable instructions stored in the memory, the at least one processor executes the information processing method described in the above first aspect and various possible designs of the first aspect.

[0121] In a fourth aspect, according to one or more embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

[0122] In a fifth aspect, according to one or more embodiments of the present disclosure, a computer program product including a computer program is provided, and when the computer program is executed by a processor, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

[0123] In a sixth aspect, according to one or more embodiments of the present disclosure, a computer program is provided, and when the computer program is executed by a processor, the information processing method described in the above first aspect and various possible designs of the first aspect is realized.

[0124] The above description is only an explanation of some preferred embodiments of the present disclosure and an explanation of the applicable technical principles. Those skilled in the art should understand that the disclosure scope of the present disclosure is not limited to the solution formed by a specific combination of the above technical features, and without departing from the above disclosure concept, other solutions formed by any combination of the above technical features or their equivalent features, for example, solutions formed by replacing the above features with technical features having similar functions (but not limited to) disclosed in the present disclosure should also be covered.

[0125] Although the operations have been described in a particular order, it should not be understood that these operations are required to be performed in the particular order or sequence shown. Multitasking or parallel processing may be advantageous in certain environments. Similarly, although the above description includes some specific implementation details, these should not be construed as limiting the scope of the present disclosure. The specific features described in the context of individual embodiments may be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may be implemented separately or in any suitable subcombination in multiple embodiments.

[0126] Although the subject matter has been described in terms of language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Conversely, the above specific features and acts are merely exemplary forms for implementing the claims.

[0127] This disclosure claims priority to a Chinese patent application filed with the Chinese Patent Office on September 15, 2021, with application number 202111082885.0 and application title "Information Processing Method, Apparatus, Device, Readable Storage Medium, and Product", the entire content of which is incorporated herein by reference.

Claims

1. An information processing method, comprising: obtaining comment data including text data and / or video data and / or audio data corresponding to at least one target media content having an association with a preset object; performing an extraction operation on the comment data to obtain at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content from the comment data; intensively displaying the at least one question-and-answer content on an interface associated with the preset object.

2. The step of obtaining comment data corresponding to at least one target media content includes: obtaining at least one target media content with at least one interaction data number exceeding a preset comment number threshold and / or at least one target media content with a playback number exceeding a preset playback number threshold; obtaining the comment data of the at least one target media content. The method according to claim 1.

3. The target media content includes a label corresponding to the preset object, and / or the text content corresponding to the target media content includes a field associated with the preset object, and / or the comment data corresponding to the target media content includes a field associated with the preset object, and / or the image content corresponding to the target media content includes an image corresponding to the preset object. The method according to claim 1.

4. The step of performing an extraction operation on the comment data to obtain at least one question-and-answer content associated with the preset object from the comment data includes: obtaining question data associated with the preset object from the comment data; Selecting question data in the comment data according to at least one target topic of the preset, which is a feature information topic corresponding to the object of the preset, and obtaining at least one question data; Obtaining answer data corresponding to the question data, determining whether the answer data is associated with the target topic of the preset, and if the result of the determination is that it is associated with the target topic of the preset, determining the answer data as target answer data corresponding to the question data; Determining the at least one question data and at least one target answer data corresponding to each question data as the at least one question-and-answer content, the method according to claim 1.

5. The step of determining whether the answer data is associated with the target topic of the preset includes: Determining whether the answer data includes the target topic of the preset, and / or determining whether the answer data includes the target field corresponding to the target topic to obtain a determination result, the method according to claim 4.

6. The step of performing an extraction operation on the comment data to obtain at least one question-and-answer content associated with the object of the preset in the comment data includes: Inputting the comment data into a question-and-answer content extraction model of the preset to obtain at least one question-and-answer content associated with the object of the preset in the comment data, wherein the question-and-answer content extraction model is obtained by training a model to be trained of the preset using a sample question-and-answer content dataset, the sample question-and-answer content dataset includes comment data corresponding to a plurality of target media contents and marking information corresponding to each comment data, and the marking information is used to characterize whether the comment data includes question-and-answer content, the method according to claim 1.

7. The step of aggregating and displaying the at least one question-and-answer content on an interface associated with the object of the preset includes: Steps of calculating similarity information between respective question-and-answer contents; Based on the similarity information, performing an aggregation operation on question-and-answer contents whose similarity exceeds a preset similarity threshold, and determining target question-and-answer contents; Displaying the target question-and-answer contents on an interface associated with the preset object, the method according to claim 1.

8. The method includes: Determining the number of question-and-answer contents whose similarity with the target question-and-answer contents exceeds a preset threshold; Updating the number of follows corresponding to the target question-and-answer contents according to the number of the question-and-answer contents and the number of user trigger operations on a follow button associated with the target question-and-answer contents, the method according to claim 7.

9. After aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object, Updating the interaction number of the question-and-answer content according to the interaction number of the question-and-answer content in the interface associated with the preset object and the interaction number of the corresponding comment data of the question-and-answer content in the target media content, the method according to claim 1.

10. Further including, in response to a deletion operation of comment data of target media content by a user, performing a deletion operation on the question-and-answer content corresponding to the comment data, the method according to claim 1.

11. An information processing apparatus, An acquisition module for acquiring comment data including text data and / or video data and / or audio data corresponding to at least one target media content having an association relationship with a preset object; An extraction module for performing an extraction operation on the comment data to acquire at least one question-and-answer content including question content associated with the preset object and at least one answer content for the question content in the comment data; A display module for aggregating and displaying the at least one question-and-answer content on an interface associated with the preset object, and an information processing apparatus including the same. **Claim 12** An electronic device including a processor and a memory, wherein the memory stores computer-executable instructions, and when the processor executes the computer-executable instructions stored in the memory, the processor executes the information processing method according to any one of claims 1 to 10. **Claim 13** A computer-readable storage medium storing computer-executable instructions, and when a processor executes the computer-executable instructions, the information processing method according to any one of claims 1 to 10 is realized. **Claim 14** A computer program, and when the computer program is executed by a processor, the information processing method according to any one of claims 1 to 10 is realized.

Images