CN111866578A

CN111866578A - Data processing method and device, electronic equipment and computer readable storage medium

Info

Publication number: CN111866578A
Application number: CN201911408496.5A
Authority: CN
Inventors: 王瑜; 李敏; 郭瑞; 叶舟; 黄文强; 陈爱萍
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-12-31
Filing date: 2019-12-31
Publication date: 2020-10-30

Abstract

The embodiment of the application provides a data processing method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of data processing. First, all comment data for a video is acquired. And secondly, screening each evaluation data. And then, displaying the screened comment data and the video. By the method, the display rationality of the comment data is improved, and therefore the user experience is improved.

Description

Data processing method and device, electronic equipment and computer readable storage medium

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method and apparatus, an electronic device, and a computer-readable storage medium.

Background

Currently, in the live broadcast process of a network platform, audiences can communicate with a main broadcast or evaluate the main broadcast in a manner of making comments. When the live broadcast is finished, a live video for recording the live broadcast process can be generated, and comments sent by the audience can be left in the live video. If the number of comments is large, a great number of comments appear in the live video, even the original video content is covered, so that the user cannot clearly see the original video content when watching the live video.

However, the inventor researches and finds that in the prior art, all comments are watched or no comment is watched, so that the user experience is poor.

Disclosure of Invention

In view of the above, an object of the present application is to provide a data processing method and apparatus, an electronic device, and a computer-readable storage medium, so as to solve the problems in the prior art.

In order to achieve the above purpose, the embodiment of the present application adopts the following technical solutions:

a method of data processing, comprising:

acquiring all comment data aiming at the video;

screening each evaluation data;

and displaying the screened comment data and the video.

In a preferred selection of the embodiment of the present application, the step of performing a screening process on each of the evaluation data includes:

determining the total amount of the comment data to be screened out according to the duration of the video;

and screening the total number of comment data from each comment data.

calculating the total amount of the comment data to be screened out according to the duration and the preset frequency of the video;

And screening the total number of comment data from each comment data.

calculating the comment density of the comment data according to the number of all comment data and the duration of the video;

calculating to obtain a target density according to the comment density and a preset coefficient;

calculating the total amount of the comment data to be screened out according to the video duration and the target density;

and screening the total number of comment data from each comment data.

In a preferred selection of the embodiment of the present application, the step of screening out the total number of review data from each of the review data includes:

classifying the comment data according to keywords of the comment data;

and screening the comment data aiming at each category to obtain the comment data of the total amount.

In a preferred selection of the embodiment of the present application, the comment data includes comment content, and the step of classifying each comment data according to a keyword of each comment data includes:

Performing keyword extraction processing on the comment content of each comment data to obtain a keyword of each comment data;

and classifying each piece of comment data according to the keyword of each piece of comment data.

In a preferred selection of the embodiment of the present application, the step of performing keyword extraction processing on the comment content of each comment data includes:

performing word segmentation on the comment content of each comment data to obtain at least one comment vocabulary;

and matching the at least one comment vocabulary with at least one category of preset keywords.

In a preferred selection of the embodiment of the present application, the step of performing screening processing on the comment data for each category to obtain the total number of comment data includes:

calculating the quantity proportion of the comment data of each category according to the quantity of the comment data included in the category and the total quantity for each category;

calculating the quantity of the comment data after the category screening processing according to the quantity proportion of the comment data of the category and the quantity of the comment data of the category;

and screening the comment data belonging to the category according to the number of the comment data subjected to the category screening processing to obtain the total number of comment data.

In a preferred selection in the embodiment of the present application, the step of performing screening processing on the comment data belonging to the category according to the number of the comment data subjected to the screening processing of the category to obtain the total number of comment data includes:

ranking the comment data of each category;

and screening the sorted comment data of each category according to the number of the comment data screened and processed by each category to obtain the comment data of the total number.

In a preferred selection of the embodiment of the present application, the step of performing ranking processing on the comment data of each category includes:

for each category, obtaining the rating data of each comment data included in the category;

and sequencing each comment data included in each category according to the grading data.

In a preferred selection of the embodiment of the present application, the step of obtaining, for each category, score data of each review data included in the category includes:

and scoring each comment data included in the category according to a preset rule to obtain the scoring data of each comment data.

In a preferred selection of the embodiment of the present application, the step of displaying the comment data after the filtering processing and the video includes:

Judging whether the grade data of the comment data exceeds preset grade data or not according to each piece of comment data after screening processing;

if the rating data of the comment data exceed the preset rating data, highlighting the comment data in the video.

and combining the screened comment data with the video for display in a bullet screen mode.

In a preferred selection of the embodiment of the present application, the comment data includes comment time, and the step of displaying the comment data after the filtering processing and the video includes:

and displaying the comment data subjected to the screening processing according to the comment time of the comment data and the video.

An embodiment of the present application further provides a data processing apparatus, including:

the data acquisition module is used for acquiring all comment data aiming at the video;

the data processing module is used for screening each piece of comment data;

and the data display module is used for displaying the screened comment data and the video.

In a preferred option of the embodiment of the present application, the data processing module includes:

the total number determining submodule is used for determining the total number of the comment data to be screened out according to the duration of the video;

and the screening processing submodule is used for screening the total number of the comment data from each comment data.

the first total number calculating submodule is used for calculating the total number of the comment data to be screened out according to the duration and the preset frequency of the video;

the comment density calculation submodule is used for calculating the comment density of the comment data according to the number of all the comment data and the duration of the video;

the target density calculation submodule is used for calculating to obtain a target density according to the comment density and a preset coefficient;

the second total number calculating submodule is used for calculating the total number of the comment data to be screened out according to the video duration and the target density;

An embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the processor is configured to execute an executable computer program stored in the memory, so as to implement the data processing method described above.

Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed, the steps of the data processing method are implemented.

The data processing method and device, the electronic device and the computer readable storage medium provided by the embodiment of the application can display the comment data and the video after screening processing, and improve the display rationality of the comment data, so that the user experience is improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is an interaction schematic block diagram of a data processing system according to an embodiment of the present application.

Fig. 2 is a schematic diagram of exemplary hardware and software components of an electronic device that may implement the server and the service requester terminal of fig. 1 according to an embodiment of the present disclosure.

Fig. 3 shows one of the flowcharts of the data processing method provided in the embodiment of the present application.

Fig. 4 shows a second flowchart of the data processing method according to the embodiment of the present application.

Fig. 5 shows a third flowchart of the data processing method according to the embodiment of the present application.

Fig. 6 shows a fourth flowchart of the data processing method according to the embodiment of the present application.

Fig. 7 shows a fifth flowchart of the data processing method according to the embodiment of the present application.

Fig. 8 shows one of functional block diagrams of a data processing apparatus according to an embodiment of the present application.

Icon: 100-a data processing system; 110-a server; 120-a network; 130-service requester terminal; 140-a database; 200-an electronic device; 210-a network port; 220-a processor; 230-a communication bus; 240-storage medium; 250-an interface; 300-a data processing apparatus; 310-a data acquisition module; 320-a data processing module; 330-data display module.

Detailed Description

At present, a plurality of network platforms all provide a live video function, a main broadcast shoots videos through a mobile phone or a camera connected with a computer and pushes the videos to a network in real time, and audiences can watch the videos sent by the main broadcast in real time through the network. During the live broadcast process, the audience can communicate with the anchor or evaluate the anchor by making comments. The comments posted by the audience may generally appear below the video and automatically disappear after being displayed for a period of time, or the last comment automatically disappears when the updated comment is being traveled.

In general, if there are many viewers, there are many comments transmitted during the live broadcast. In the prior art, after live broadcasting is finished, live broadcasting video for recording a live broadcasting process can be generated, and comments sent by audiences are left in the live broadcasting video in a bullet screen mode. If the number of comments is large, a great number of barrage comments can appear in the live video, even the original video content is covered, so that the user cannot clearly see the original video content when watching the live video.

In order to solve the problem that the bullet screen interferes the user to normally watch the video, the prior art provides the function of closing the bullet screen or hiding the bullet screen, namely hiding all bullet screen information. In the scheme of the prior art, the barrage comment can not interfere with the normal video watching of the user only by hiding all the barrage comments, but the user cannot see the barrage comment information, so that the user experience is poor.

In order to improve at least one technical problem provided by the present application, embodiments of the present application provide a data processing method and apparatus, an electronic device, and a computer-readable storage medium, which can display the comment data after the screening processing and the video, so as to improve the reasonability of comment data display, thereby improving user experience.

The technical solution of the present application is explained below by means of possible implementations.

The defects of the above solutions are the results of the inventor after practice and careful study, and therefore, the discovery process of the above problems and the solution proposed by the present application to the above problems should be the contribution of the inventor to the present application in the process of the present application.

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

In order to enable a person skilled in the art to make use of the present disclosure, the following embodiments are given. It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Applications of the system or method of the present application may include web pages, plug-ins for browsers, client terminals, customization systems, internal analysis systems, or artificial intelligence robots, among others, or any combination thereof.

It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.

The terminology used in the following examples is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of this application and the appended claims, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, such as "one or more", unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of the present application, "at least one", "one or more" means one, two or more. The term "and/or" is used to describe an association relationship that associates objects, meaning that three relationships may exist; for example, a and/or B, may represent: a alone, both A and B, and B alone, where A, B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.

FIG. 1 is an exemplary block diagram of a data processing system 100 of some embodiments of the present application. Data processing system 100 may be used for various types of software service platforms. For example, an online live service platform may be used for live services such as game live, digital live, car live, talk show live, or any combination thereof. Data processing system 100 may include one or more of a server 110, a network 120, a service requester terminal 130, and a database 140, and server 110 may include a processor that performs operations on instructions.

In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, the server 110 may access information and/or data stored in the service requester terminal 130 via the network 120. As another example, the server 110 may be directly connected to the service requester terminal 130 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a resilient cloud, a community cloud (community cloud), a distributed cloud, a cross-cloud (inter-cloud), a multi-cloud (multi-cloud), and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.

In some embodiments, the server 110 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. In some embodiments, a processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer, RISC), a microprocessor, or the like, or any combination thereof.

Network 120 may be used for the exchange of information and/or data. In some embodiments, one or more components in data processing system 100 (e.g., server 110 and service requester terminal 130) may send information and/or data to other components. For example, the server 110 may obtain a service request from the service requester terminal 130 via the network 120. In some embodiments, the network 120 may be any type of wired or wireless network, or combination thereof. Merely by way of example, Network 120 may include a wired Network, a Wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a WLAN, a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof.

In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of data processing system 100 may connect to network 120 to exchange data and/or information.

In some embodiments, the user of the service requestor terminal 130 may be someone other than the actual demander of the service. For example, the user a of the service requester terminal 130 may use the service requester terminal 130 to initiate a service request for the service actual demander B, or receive service information or instructions or the like from the server 110. In some embodiments, "service requester" and "service requester terminal" may be used interchangeably.

In some embodiments, the service requestor terminal 130 may comprise a mobile device, a tablet computer, a laptop computer, or a built-in device in a motor vehicle, etc., or any combination thereof, as long as live video may be displayed for viewing by a user and comment data for the user may be received. In some embodiments, the mobile device may include a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include control devices of smart electrical devices, smart monitoring devices, smart televisions, smart cameras, and the like, or any combination thereof. In some embodiments, the wearable device may include smart glasses, smart helmets, smart watches, smart clothing, smart backpacks, smart accessories, and the like, or any combination thereof. In some embodiments, the smart mobile device may include a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or augmented reality device may include various virtual reality products and the like. In some embodiments, the built-in devices in the motor vehicle may include an on-board computer, an on-board television, and the like.

A database 140 may be included in server 110, and database 140 may store data and/or instructions. In some embodiments, the database 140 may store data obtained from the service requester terminal 130. In some embodiments, database 140 may store data and/or instructions for the exemplary methods described herein. In some embodiments, the database 140 may include mass storage, removable storage, volatile Read-write memory, Read-only memory (ROM), or the like, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write Memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, database 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, cross-cloud, multi-cloud, elastic cloud, or the like, or any combination thereof.

In some embodiments, a database 140 may be connected to network 120 to communicate with one or more components in data processing system 100 (e.g., server 110 and service requester terminal 130). One or more components in data processing system 100 may access data or instructions stored in database 140 via network 120. In some embodiments, database 140 may be directly connected to one or more components in data processing system 100 (e.g., server 110 and service requester terminal 130). Alternatively, in some embodiments, database 140 may also be part of server 110.

In some embodiments, one or more components in data processing system 100 (e.g., server 110 and service requester terminal 130) may have access to database 140. In some embodiments, one or more components in data processing system 100 may read and/or modify information related to the service requester or the public, or any combination thereof, when certain conditions are met. For example, server 110 may read and/or modify information for one or more users after receiving a service request.

In some embodiments, the exchange of information by one or more components in data processing system 100 may be accomplished by a request service. The object of the service request may be any product. In some embodiments, the product may be a tangible product or a non-physical product. Tangible products may include food, pharmaceuticals, commodities, chemical products, appliances, clothing, automobiles, homes, or luxury goods, and the like, or any combination thereof. The non-material product may include a service product, a financial product, a knowledge product, an internet product, or the like, or any combination thereof. The internet product may include a stand-alone host product, a network product, a mobile internet product, a commercial host product, an embedded product, or the like, or any combination thereof. The internet product may be used in software, programs, or systems of the mobile terminal, etc., or any combination thereof. The mobile terminal may include a tablet, a laptop, a mobile phone, a Personal Digital Assistant (PDA), a smart watch, a Point of sale (POS) device, a vehicle-mounted computer, a vehicle-mounted television, a wearable device, or the like, or any combination thereof. The internet product may be, for example, any software and/or application used in a computer or mobile phone. The software and/or applications may relate to social interaction, shopping, transportation, entertainment time, learning, or investment, or the like, or any combination thereof. In some embodiments, the transportation-related software and/or applications may include travel software and/or applications, vehicle dispatch software and/or applications, mapping software and/or applications, and the like. In the vehicle scheduling software and/or application, the vehicle may include a horse, a carriage, a human powered vehicle (e.g., unicycle, bicycle, tricycle, etc.), an automobile (e.g., taxi, bus, privatege, etc.), a train, a subway, a ship, an airplane (e.g., airplane, helicopter, space shuttle, rocket, hot air balloon, etc.), etc., or any combination thereof.

Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device 200 of a server 110, a service requester terminal 130, which may implement the concepts of the present application, according to some embodiments of the present application. For example, the processor 220 may be disposed on the electronic device 200 and configured to perform the functions of the present application.

The electronic device 200 may be a general-purpose computer or a special-purpose computer, both of which may be used to implement the data processing method of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.

For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).

For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.

Fig. 3 shows one of the flowcharts of the data processing method provided in the embodiment of the present application. The method may be applied to the server 110 in fig. 1, and is performed by the server 110 shown in fig. 1. It should be understood that, in other embodiments, the order of some steps in the data processing method described in this embodiment may be interchanged according to actual needs, or some steps may be omitted or deleted. The flow of the data processing method shown in fig. 3 is described in detail below.

In step S110, all comment data for the video are acquired.

The comment data may be initiated by the user through the service requester terminal 130, for example, when the user sends the comment data, the comment data is requested to the server 110 through the service requester terminal 130, and the server 110 obtains the comment data.

And step S120, screening each evaluation data.

In the embodiment of the present application, after all the comment data are obtained in step S110, a screening process may be performed on each of the comment data.

And step S130, displaying the screened comment data and the video.

In the embodiment of the present application, after each of the comment data is subjected to the filtering processing in step S120, the filtered comment data and the video may be displayed.

By the method, the comment data after screening processing and the video can be displayed, the display rationality of the comment data is improved, and therefore user experience is improved.

For step S110, it should be noted that, as an alternative implementation, all the comment data for the video may be acquired at set time intervals in the present embodiment. The time interval for acquiring each comment data for a video may be the same or different. For example, comment data for a video may be acquired at a fixed time interval. For another example, the comment data for the video may be acquired at non-fixed time intervals.

In one implementation, obtaining review data for a video at non-fixed time intervals may include: the time interval for acquiring the comment data for the video may be reduced if the number of the comment data continues to increase as obtained from the analysis of the comment data for the video, and the time interval for acquiring the comment data for the video may be increased if the number of the comment data does not continue to increase as obtained from the analysis of the comment data for the video. For example, if the comment data for the video is acquired every 10 seconds from the time point 14, 10 minutes and 50 seconds, and the statistics at the time point 14, 20 minutes and 50 seconds show that the number of comment data for the video acquired every 10 seconds in 10 minutes between the time point 14, 10 minutes and 50 seconds and the time point 14, 20 minutes and 50 seconds continues to increase, the comment data for the video is adjusted to be acquired every 6 seconds from the time point 14, 20 minutes and 50 seconds, thereby reducing the time interval for acquiring the comment data. Similarly, if statistics indicate at time 14, point 25 min 50 sec that the amount of review data acquired every 6 seconds in 5 minutes between time 14, point 20 min 50 sec to time 14, point 25 min 50 sec continues to increase (and the rate of increase is faster), then, starting at time 14, point 25 min 50 sec, the review data for the video is adjusted to be acquired every 2 seconds, thereby further reducing the time interval for acquiring the review data. Correspondingly, if the statistics of 30 minutes and 25 seconds at the time point 13 shows that the number of the comment data obtained in the last 1 minute every 2 seconds is not continuously increased through analysis, the comment data for the video is obtained every 12 seconds. It should be understood that the above example is only illustrative, and the time interval for obtaining the comment data may be flexibly adjusted in feedback in other ways based on the analysis result of whether the number of the comment data is continuously increased.

For step S120, it should be noted that, the specific manner of performing the screening processing on each of the evaluation data is not limited, and may be set according to the actual application requirement.

For example, in an alternative example, a specific manner of performing the screening process on each of the evaluation data is provided, and in conjunction with fig. 4, step S120 may include step S121 and step S126.

And step S121, determining the total amount of the comment data to be screened out according to the duration of the video.

Step S126, screening the total number of the comment data from each comment data.

For step S121, it should be noted that the total amount of the comment data to be screened out may be determined according to the duration of the video and a pre-stored video duration-comment data total amount template.

The video duration-total comment data quantity template pre-stored by the server 110 can be obtained in various ways.

For example, the server 110 may create a video duration-total number of comment data template according to comment data input by a user and store the video duration-total number of comment data template, so as to obtain a pre-stored video duration-total number of comment data template. Among them, the user can input comment data through the service requester terminal 130, and the comment data input by the user is transmitted to the server 110 by the service requester terminal 130.

For another example, the server 110 may analyze the creation mode of the total number of comment data provided by each live broadcast platform through big data collection, collect one or more video duration-comment data total number templates corresponding to the creation mode of the total number of comment data provided by each live broadcast platform, and store all the collected video duration-comment data total number templates to obtain a pre-stored video duration-comment data total number template.

For another example, the server 110 may present the stored video duration-total comment data quantity templates (which may include one or more video duration-total comment data quantity templates obtained by the server 110 through big data collection and aggregation, and may also include a video duration-total comment data quantity template created by the server 110 according to comment data input by the user). Specifically, the server 110 may send the stored video duration-total comment data quantity template to the service requester terminal 130 of the user for displaying, so that the user can perform custom modification on the stored video duration-total comment data quantity template through the service requester terminal 130, and the service requester terminal 130 sends the modification information to the server 110. The server 110 then stores the modified video duration-total number of comment data templates to obtain the pre-stored video duration-total number of comment data templates.

For another example, the user may create a template of the video duration-total number of comment data in a customized manner, and the server 110 stores the template of the video duration-total number of comment data created in the customized manner by the user, so as to obtain a pre-stored template of the video duration-total number of comment data.

The video duration-total comment data quantity template comprises a plurality of video durations and the corresponding total quantity of comment data. For example, when the video duration is 1 minute, the total number of pieces of comment data to be screened out may be 100. For another example, when the video duration is 10 minutes, the total number of pieces of comment data to be filtered out may be 1000.

The mode that the server 110 creates a video duration-total number of comment data template according to the comment data input by the user and stores the video duration-total number of comment data template can be flexibly set.

As one optional implementation manner, in order to increase the comprehensiveness of the pre-stored video duration-total number of comment data templates as much as possible, the server 110 may multiply a coefficient smaller than 1 by the total number of comment data input by the user to obtain the total number of comment data to be screened, and after obtaining the total number of comment data to be screened, may directly create a video duration-total number of comment data template and store the video duration-total number of comment data template as the pre-stored video duration-total number of comment data template.

Similarly, when there is a user operation to modify the stored video duration-total comment data quantity template, the server 110 may directly store the modified video duration-total comment data quantity template as a pre-stored video duration-total comment data quantity template according to the user operation to modify the video duration-total comment data quantity template. For example, after the server 110 sends the stored video duration-total comment data quantity template to the service requester terminal 130 of the user for display, if the video duration-total comment data quantity template cannot completely meet the requirement, the user may modify the stored video duration-total comment data quantity template on the service requester terminal 130, where the modification may include modifying, adding, deleting, and the like the original information. After the modification is completed, the service requester terminal 130 sends the user operation for modifying the video duration-total comment data quantity template to the server 110, and the server 110 directly stores the modified video duration-total comment data quantity template as a pre-stored video duration-total comment data quantity template according to the user operation for modifying the video duration-total comment data quantity template.

Under the condition that the server 110 directly creates the video duration-total number of comment data template according to the comment data input by the user, the server 110 can judge whether the same video duration-total number of comment data template is already stored, and if the same video duration-total number of comment data template is judged to be already stored, the server 110 does not repeatedly store the video duration-total number of comment data template. Therefore, resource waste caused by repeated storage of the same video time length-comment data total number template is avoided, and the effective utilization rate of the storage space is ensured.

In the case that the server 110 directly creates a video duration-total number of comment data template according to comment data input by a user, and the server 110 determines that the same video duration-total number of comment data template has been stored, the server 110 may further send prompt information indicating that the video duration-total number of comment data template has been stored. For example, the prompt information indicating that the video duration-comment data total amount template has been stored is sent to the service requester terminal 130 of the user. Based on the prompt information, it may indicate that the user server 110 will not repeatedly store the same video duration-total number of comment data template any more, and may indicate that the user can perform modification operation on the video duration-total number of comment data template.

Under the condition that the server 110 directly creates a video duration-total number of comment data template according to comment data input by a user, if the server 110 determines that a video duration-total number of comment data template which is the same as the video duration-total number of comment data template created according to the comment data input by the user is not stored, the server 110 may directly create and store the video duration-total number of comment data template according to the comment data input by the user.

When the server 110 directly creates the video duration-total number of comment data template according to the comment data input by the user, a prompt message can be sent to remind the user that the video duration-total number of comment data template is to be automatically created and stored, so that data processing can be performed next time conveniently. The prompt information can also indicate that the user can choose to reject the creation and storage of the video duration-total comment data number template, the server 110 does not create the video duration-total comment data number template in the case that the user chooses to reject the creation and storage of the video duration-total comment data number template, and the server 110 defaults to create and store the video duration-total comment data number template in the case that the user does not choose to reject the creation and storage of the video duration-total comment data number template.

Similarly, when the server 110 detects a user operation for modifying the stored video duration-total comment data quantity template, the server 110 may also determine whether the modified video duration-total comment data quantity template is stored, and if the same video duration-total comment data quantity template as the modified video duration-total comment data quantity template is already stored in the server 110, the server 110 does not repeatedly store the video duration-total comment data quantity template. In the case that the same video duration-total number of comment data template as the modified video duration-total number of comment data template has already been stored in the server 110, the server 110 may also send the stored prompt information of the video duration-total number of comment data template. If the video duration-total comment data quantity template which is the same as the modified video duration-total comment data quantity template is not stored in the server 110, the server 110 can directly store the video duration-total comment data quantity template. Since the corresponding process is similar to the above process of directly creating the video duration-total number of comment data template according to the comment data input by the user, further description is omitted here.

Further, in another alternative example, a specific manner of performing the screening process on each of the evaluation data is also provided, and in conjunction with fig. 5, step S120 may further include step S122 and step S126.

And S122, calculating the total amount of the comment data to be screened out according to the duration and the preset frequency of the video.

For step S122, it should be noted that the specific size of the preset frequency is not limited, and may be set according to the actual application requirement.

For example, in an alternative example, when a user watches a live video, it is preferable that a piece of comment content appears for 2 minutes, which neither interferes with the user's viewing, but enables the user to see the video, i.e., the preset frequency may be 2 minutes/piece. Therefore, the duration of the video may be divided by 2 minutes to obtain the total amount of the comment data to be screened out. When the video duration is 10 minutes, calculating to obtain 5 pieces of comment data to be screened out; and when the video duration is 100 minutes, calculating to obtain 50 pieces of total number of the comment data to be screened.

Further, in another alternative example, a specific manner of performing the screening process on each of the evaluation data is also provided, and with reference to fig. 6, step S120 may further include step S123, step S124, step S125, and step S126.

And S123, calculating the comment density of the comment data according to the number of all the comment data and the duration of the video.

And step S124, calculating to obtain a target density according to the comment density and a preset coefficient.

And step S125, calculating the total amount of the comment data to be screened out according to the duration of the video and the target density.

Generally, the more the total amount of comment data published by the user, the more the excellent comment content is included, and therefore, the more comments can be left after the filtering process. First, the comment density can be obtained by dividing the total number of comments by the time length of the live video. And then multiplying the comment density by a preset coefficient to obtain a target density (the preset coefficient is less than or equal to 1). And then, multiplying the target density by the time length of the live video so as to obtain the total amount of the comment data to be screened out.

The preset coefficient may be related to the calculated comment density, for example, the larger the comment density is, the smaller the coefficient is, so as to reduce the density.

For example, in an alternative example, first, the number of all comment data may be 10000, the duration of the video may be 10 minutes, and the comment density of the comment data may be calculated to be 1000 pieces/minute according to the number of all comment data and the duration of the video. Secondly, the comment density is larger, and the preset coefficient may be a smaller value, for example, the preset coefficient may be 0.01. And calculating to obtain the target density which can be 10 strips/minute according to the comment density and a preset coefficient. Then, according to the duration of the video and the target density, the total number of the comment data to be screened out through calculation can be 100.

For another example, in another alternative example, first, the number of all the comment data may be 1000, the duration of the video may be 100 minutes, and the comment density of the comment data calculated according to the number of all the comment data and the duration of the video may be 10 pieces/minute. Secondly, the comment density is small, and the preset coefficient may be a large value, for example, the preset coefficient may be 0.5. And calculating to obtain the target density which can be 5 strips/minute according to the comment density and a preset coefficient. Then, according to the duration of the video and the target density, the total number of the comment data to be screened out through calculation can be 500.

For the step S126, it should be noted that, a specific manner of screening the total number of the comment data from each of the comment data is not limited, and may be set according to an actual application requirement.

For example, in an alternative example, in conjunction with fig. 7, step S126 may include step S1261 and step S1262.

Step S1261, classify each of the comment data according to the keyword of each of the comment data.

Step S1262, for each category, performing screening processing on the comment data respectively to obtain the total amount of comment data.

For step S1261, it should be noted that, the specific manner of classifying the comment data according to the keyword of each comment data is not limited, and may be set according to actual application requirements.

For example, in an alternative example, when the comment data includes comment content, step S1261 may include the following sub-steps:

firstly, keyword extraction processing is carried out on the comment content of each comment data to obtain the keyword of each comment data. Secondly, classifying each piece of comment data according to the keyword of each piece of comment data.

It should be noted that the specific content of the comment data is not limited, and may be set according to the actual application requirement. For example, information may include, but is not limited to, comment content, comment time, user ID, and the like. For example, user a sends comment data with comment content of "good congestion on the road today" at 14 hours, 28 minutes, and 33 seconds, and the comment data can be expressed as { good congestion on the road today; 14 hours, 28 minutes, 33 seconds; user a's ID }.

The specific way of extracting the keywords from the comment content of each comment data is not limited, and the keyword extraction processing can be set according to actual application requirements.

For example, in an alternative example, the step of performing keyword extraction processing on the comment content of each of the comment data may include the sub-steps of:

firstly, word segmentation processing is carried out on the comment content of each comment data to obtain at least one comment vocabulary. And secondly, matching the at least one comment vocabulary with at least one category of preset keywords.

For example, in live traffic, the keywords of the traffic category may include, but are not limited to, words used for describing traffic conditions, such as traffic jam, smooth, clear, more vehicles, less vehicles, traffic jam, and the like. If the comment content included in the comment data sent by the user is 'traffic congestion for a while', the comment vocabulary obtained after the word segmentation processing can be 'traffic congestion for a while', 'traffic congestion' and 'traffic congestion', the comment vocabulary is matched with the keywords of the traffic condition category, and the keywords of the comment data are 'traffic congestion'.

For another example, in a live game, the keywords of the bonus category may include, but are not limited to, words describing the bonus, prize, draw, prize, etc. If the comment content sent by the user is 'when to start lottery', the comment vocabulary obtained after the word segmentation processing is 'when', 'start', 'lottery' and 'o', the comment vocabulary is matched with the keywords of the bonus category, and the keywords of the comment data are 'lottery'.

The preset keywords of the at least one category can be obtained in various ways.

For example, the server 110 may create a preset keyword database according to a preset keyword input by a user and store the preset keyword database, so as to obtain at least one category of prestored keywords. Wherein the user can input at least one category of keywords through the service requester terminal 130, and the service requester terminal 130 transmits the keywords input by the user to the server 110.

For another example, the server 110 may present the stored keywords (which may include one or more keywords collected and summarized by the server 110 through big data collection, or may include a preset keyword database created by the server 110 according to the keywords input by the user). Specifically, the server 110 may send the stored keywords to the service requester terminal 130 of the user for presentation, so that the user can perform customized modification on the stored keywords through the service requester terminal 130, and the service requester terminal 130 sends the modification information to the server 110. The server 110 then stores the modified preset keyword database to obtain at least one category of pre-stored keywords.

For another example, the user may create a preset keyword database in a user-defined manner, and the server 110 stores the preset keyword database created in the user-defined manner, so as to obtain at least one category of pre-stored keywords.

It should be noted that the server 110 may perform matching processing on the at least one comment vocabulary and the preset keyword of each category, respectively, so as to classify the comment data in detail. That is, the comment data may belong to one category or may belong to a plurality of categories.

Since the keywords correspond to the categories, after the keywords of each piece of review data are obtained, each piece of review data can be classified according to the keywords of each piece of review data. For example, if the comment content included in the comment data sent by the user is "traffic congestion for a while", the comment vocabulary obtained after the word segmentation process is matched with the keyword of the traffic condition category, and the keyword of the comment data obtained is "traffic congestion". The keyword 'traffic jam' belongs to the road condition category, and the category of the comment data is the road condition category.

Some users may send repeated words or punctuation marks, such as a series of exclamation marks, while watching the live broadcast, and may sort such comment data individually.

Further, the comment data are screened for each category, and the specific way of obtaining the total number of comment data is not limited and can be set according to actual application requirements.

For example, in an alternative example, step S1262 may include the following sub-steps:

firstly, for each category, calculating a quantity proportion of the comment data of the category according to the quantity of the comment data included in the category and the total quantity. And secondly, calculating to obtain the number of the comment data after the category screening processing according to the number proportion of the comment data of the category and the number of the comment data of the category. And then, screening the comment data belonging to the category according to the number of the comment data subjected to the category screening processing to obtain the total number of comment data.

For example, the category a includes 1000 pieces of comment data, the category B includes 500 pieces of comment data, the category C includes 1500 pieces of comment data, the total number of pieces of comment data screened from the respective pieces of comment data is 300, and the percentage of the number of pieces of comment data of each category is calculated to be 10%. Secondly, according to the quantity proportion of the comment data of each category and the quantity of the comment data of each category, the quantity of the comment data obtained after the category A screening processing is 100, the quantity of the comment data obtained after the category B screening processing is 50, and the quantity of the comment data obtained after the category C screening processing is 150.

If the number ratio of the comment data of the category is high, the viewer can be considered to pay more attention to the category, and more comments of the category can be left. If the ratio of the number of the comment data of the category is low, the viewer may be considered to be less interested in the category, and may leave less comments of the category. When the number of the comment data after the screening process for each category obtained by the calculation is a decimal, the rounding process may be performed.

Further, the comment data belonging to the category are screened according to the number of the comment data after the category screening, and the specific manner of obtaining the total number of the comment data is not limited and can be set according to actual application requirements.

For example, in an alternative example, the review data belonging to each category may be randomly filtered according to the number of the review data after the filtering processing of each category, so as to obtain the total number of the review data.

For another example, in another alternative example, the step of filtering the comment data belonging to each category according to the number of the comment data subjected to the filtering processing for each category may include the following sub-steps:

First, ranking processing is performed on comment data for each category. And secondly, screening the sorted comment data of each category according to the number of the comment data screened by each category to obtain the total number of comment data.

The specific mode of sorting the comment data of each category is not limited, and the comment data can be set according to actual application requirements.

For example, in an alternative example, the ranking process may be performed according to the length of the comment content included in the comment data of each category to leave more comment data of the content. The road condition category can comprise comment data A, comment data B and comment data C, the comment content of the comment data A is that traffic congestion is caused for a while, the comment content of the comment data B is that traffic congestion does not occur today, the comment content of the comment data C is that traffic congestion occurs on a Monday at ordinary times and traffic congestion does not occur today, and after sorting processing, the comment data C > the comment data A > the comment data B in sequence.

For another example, in another alternative example, the step of sorting the comment data of each category may include the sub-steps of:

First, for each category, score data of each comment data included in the category is acquired. And secondly, sequencing each comment data included in each category according to the grading data.

The specific manner of obtaining the rating data of each comment data included in each category is not limited, and may be set according to actual application requirements.

For example, in an alternative example, each comment data may be scored by a user, and the user may score indices such as whether or not a word included in the comment content of each comment data is positive, the number of words of the comment content, the degree of repetition of the comment content, and the like, to obtain score data of each comment data.

For another example, in another alternative example, the step of obtaining, for each category, the score data of each comment data included in the category may include the sub-steps of:

That is, the server 110 may perform scoring processing on each comment data according to a preset rule, to obtain scoring data of each comment data. The preset rule may mean that the server 110 stores a preset scoring template, and the scoring data of each piece of comment data may be obtained by calculation according to the preset scoring template and the comment data.

For another example, the server 110 may score the comment data after the user sends the comment data, and the server 110 may update the score data of the comment data when another user further evaluates the comment data.

When the comment contents of two or more pieces of comment data are repeated, the comment data whose contents are repeated may be merged, and an average of a plurality of the comment data may be classified as score data of the merged comment data.

For step S130, it should be noted that the specific way of displaying the comment data after the filtering process and the video is not limited, and the comment data may be set according to the actual application requirement.

For example, in an alternative example, step S130 may include the following sub-steps:

Also, in order to highlight the comment data with higher scores, step S130 may further include the following sub-steps:

firstly, judging whether the grade data of each piece of comment data after screening processing exceeds preset grade data. And secondly, if the rating data of the comment data exceed the preset rating data, highlighting the comment data in the video.

For example, if the preset score data is 80 points, the comment data of the comment data a is 70 points, the comment data of the comment data B is 90 points, and the comment data of the comment data C is 82 points, the comment data B and the comment data C can be highlighted. Moreover, according to the difference between the degree of the score data of the comment data B and the score data of the comment data C exceeding the preset score data, the degree (the shade of the color) of highlighting the comment data B and the comment data C may also be different, and details thereof are not repeated.

Further, when the comment data includes a comment time, step S130 may further include the sub-steps of:

For example, the comment data a after the filtering process is sent out by the user in 14 minutes and 28 seconds of the live broadcast, and the comment data a is displayed in 14 minutes and 28 seconds of the video.

Fig. 8 shows a functional block diagram of a data processing apparatus 300 according to some embodiments of the present application. The functions performed by the data processing apparatus 300 correspond to the steps performed by the above-described method. The data processing apparatus 300 may be understood as the server 110 or a processor of the server 110, or may be understood as a component that is independent of the server 110 or the processor and that implements the functions of the present application under the control of the server 110. As shown in fig. 8, the data processing apparatus 300 may include a data acquisition module 310, a data processing module 320, and a data display module 330.

The data obtaining module 310 is configured to obtain all comment data for the video. In this embodiment, the data obtaining module 310 may be configured to perform step S110 shown in fig. 3, and for the relevant content of the data obtaining module 310, reference may be made to the foregoing detailed description of step S110.

The data processing module 320 is configured to perform screening processing on each of the evaluation data. In this embodiment, the data processing module 320 may be configured to execute step S120 shown in fig. 3, and reference may be made to the foregoing detailed description of step S120 for relevant contents of the data processing module 320.

The data display module 330 is configured to display the screened comment data and the video. In this embodiment, the data display module 330 may be configured to perform step S130 shown in fig. 3, and reference may be made to the foregoing detailed description of step S130 for relevant contents of the data display module 330.

Further, the data processing module 320 may include a total number determination sub-module and a filtering sub-module.

And the total number determining submodule is used for determining the total number of the comment data to be screened out according to the duration of the video. In this embodiment, the total number determining submodule may be configured to perform step S121 shown in fig. 4, and the foregoing detailed description of step S121 may be referred to for relevant content of the total number determining submodule.

And the screening processing submodule is used for screening the total number of the comment data from each comment data. In this embodiment, the screening processing sub-module may be configured to perform step S126 shown in fig. 4, and reference may be made to the foregoing detailed description of step S126 for relevant contents of the screening processing sub-module.

Further, the data processing module 320 may further include a first total number calculating sub-module and a screening sub-module.

And the first total number calculating submodule is used for calculating the total number of the comment data to be screened out according to the duration and the preset frequency of the video. In this embodiment, the first total number calculating submodule may be configured to execute step S122 shown in fig. 5, and the foregoing detailed description of step S122 may be referred to for relevant contents of the first total number calculating submodule.

And the screening processing submodule is used for screening the total number of the comment data from each comment data. In this embodiment, the screening processing sub-module may be configured to perform step S126 shown in fig. 5, and reference may be made to the foregoing detailed description of step S126 for relevant contents of the screening processing sub-module.

Further, the data processing module 320 may further include a comment density calculating sub-module, a target density calculating sub-module, a second total number calculating sub-module, and a screening sub-module.

And the comment density calculation submodule is used for calculating the comment density of the comment data according to the number of all the comment data and the duration of the video. In this embodiment, the comment density calculating sub-module may be configured to perform step S123 shown in fig. 6, and reference may be made to the foregoing detailed description of step S123 for relevant contents of the comment density calculating sub-module.

And the target density calculating submodule is used for calculating to obtain the target density according to the comment density and a preset coefficient. In this embodiment, the target density calculating submodule may be configured to execute step S124 shown in fig. 6, and reference may be made to the foregoing detailed description of step S124 regarding the relevant content of the target density calculating submodule.

And the second total number calculating submodule is used for calculating the total number of the comment data to be screened out according to the video duration and the target density. In this embodiment, the second total number calculating submodule may be configured to perform step S125 shown in fig. 6, and the foregoing detailed description of step S125 may be referred to for relevant contents of the second total number calculating submodule.

And the screening processing submodule is used for screening the total number of the comment data from each comment data. In this embodiment, the screening processing sub-module may be configured to perform step S126 shown in fig. 6, and reference may be made to the foregoing detailed description of step S126 for relevant contents of the screening processing sub-module.

Since the principle of the data processing apparatus 300 in the embodiment of the present application for solving the problem is similar to the data processing method described above in the embodiment of the present application, the implementation of the data processing apparatus 300 may refer to the implementation of the method, and repeated details are not repeated.

The modules may be connected or in communication with each other via a wired or wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, etc., or any combination thereof. The wireless connection may comprise a connection over a LAN, WAN, bluetooth, ZigBee, NFC, or the like, or any combination thereof. Two or more modules may be combined into a single module, and any one module may be divided into two or more units.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the data processing method.

The computer program product of the data processing method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the data processing method in the foregoing method embodiment, which may be specifically referred to in the foregoing method embodiment, and are not described herein again.

In summary, the data processing method and apparatus, the electronic device, and the computer-readable storage medium provided by the embodiment of the application can display the comment data and the video after the screening processing, so that the display rationality of the comment data is improved, and the user experience is improved.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A data processing method, comprising:

acquiring all comment data aiming at the video;

screening each evaluation data;

and displaying the screened comment data and the video.

2. The data processing method of claim 1, wherein the step of performing a screening process on each of the evaluation data comprises:

and screening the total number of comment data from each comment data.

3. The data processing method of claim 1, wherein the step of performing a screening process on each of the evaluation data comprises:

and screening the total number of comment data from each comment data.

4. The data processing method of claim 1, wherein the step of performing a screening process on each of the evaluation data comprises:

And screening the total number of comment data from each comment data.

5. The data processing method of any one of claims 2 to 4, wherein the step of screening the total number of review data from each of the review data comprises:

classifying the comment data according to keywords of the comment data;

6. The data processing method of claim 5, wherein the comment data includes comment content, and the step of classifying each comment data based on a keyword of each comment data includes:

7. The data processing method according to claim 6, wherein the step of performing keyword extraction processing on the comment content of each of the comment data includes:

8. The data processing method according to claim 5, wherein the step of performing a filtering process on the comment data for each category to obtain the total number of comment data includes:

9. The data processing method according to claim 8, wherein the step of performing a filtering process on the comment data belonging to the category according to the number of the comment data after the filtering process for the category to obtain the total number of comment data includes:

ranking the comment data of each category;

10. The data processing method of claim 9, wherein the step of performing ranking processing on the comment data of each category includes:

11. The data processing method according to claim 10, wherein the step of acquiring, for each category, score data of each comment data included in the category includes:

12. The data processing method of claim 10, wherein the step of displaying the comment data after the filtering process with the video comprises:

13. The data processing method of claim 1, wherein the step of displaying the comment data after the filtering process with the video comprises:

14. The data processing method of claim 1, wherein the comment data includes a comment time, and the step of displaying the comment data after the filtering process with the video includes:

15. A data processing apparatus, comprising:

the data processing module is used for screening each piece of comment data;

16. The data processing apparatus of claim 15, wherein the data processing module comprises:

17. The data processing apparatus of claim 15, wherein the data processing module comprises:

18. The data processing apparatus of claim 15, wherein the data processing module comprises:

19. An electronic device, comprising a memory and a processor, wherein the processor is configured to execute an executable computer program stored in the memory to implement the data processing method of any one of claims 1 to 14.

20. A computer-readable storage medium, on which a computer program is stored which, when executed, carries out the steps of the data processing method of any one of claims 1 to 14.