User activeness evaluation method and device, storage medium and electronic equipment

Abstract

The invention discloses a user activeness evaluation method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring social data of group members; preprocessing operation is carried out on the social data to obtain processed user data, and the preprocessing operation comprises at least one of data cleaning, voice conversion and expression quantification; performing calculation operation on the processed user data to obtain calculated user data, the calculation operation including at least one of the following: calculating speaking frequency, calculating speaking duration, and calculating the number of participated topics; and evaluating the activeness of the group members based on the calculated user data. Through application of the method and the device, the problems that the group member interaction quality is reduced and the group value is weakened due to the fact that the group social activity degree is not comprehensively evaluated in the related technology are solved.

Description

Background Art

[0002] With the rapid development of social media, group social networking has become an important way for people to communicate in daily life. Chat tools carry various communication scenarios such as work collaboration, interest discussions, and interactions with relatives and friends. In group social networking, accurately evaluating the activity of group members is of great significance to effective community management and clue mining.

[0003] However, there are obvious flaws in the methods of evaluating group social activity in related technologies. Traditional evaluation is often based only on the number of speeches, and this single indicator cannot fully reflect the actual participation situation. For example, situations where many speeches are made but the content is brief or where few speeches can trigger in-depth discussions are easily overlooked.

[0004] In addition, existing evaluations ignore the key factor of speaking time, which reflects investment and concentration, but is not considered by traditional methods. At the same time, insufficient attention is paid to the number of topics members participate in, whose diversity and breadth reflect interest and participation. If only focusing on the number and duration of speeches, it will lead to an inability to fully understand the members' active performance and areas of interest.

[0005] These limitations make it difficult to accurately identify active and contributing members and discover potential opinion leaders, resulting in a lack of scientific basis and pertinence in community management, making it difficult to improve the social activity and value of the group, and also affecting the discovery of key clues. Therefore, a more comprehensive, accurate and multi-factor evaluation system is urgently needed to meet the needs.

[0006] Currently, no effective solution has been proposed to address the problem in related technologies that the quality of interaction among group members declines and the value of the group weakens due to the failure to conduct a comprehensive assessment of the group's social activity. Summary of the invention

[0007] The main purpose of this application is to provide a user activity evaluation method, device, storage medium and electronic device to solve the problem in the related art that the quality of interaction among group members is reduced and the value of the group is weakened due to the failure to comprehensively evaluate the social activity of the group.

[0008] In order to achieve the above-mentioned purpose, according to the first aspect of the present application, a method for evaluating user activity is provided. The method comprises: obtaining social data of group members; performing preprocessing operations on the social data to obtain processed user data, wherein the preprocessing operations include at least one of the following: data cleaning, voice conversion, and expression quantification; performing calculation operations on the processed user data to obtain calculated user data, wherein the calculation operations include at least one of the following: calculating the frequency of speech, calculating the duration of speech, and calculating the number of topics participated in; and evaluating the activity of group members based on the calculated user data.

[0009] Further, the calculation operation is to calculate the speaking frequency. Perform a calculation operation on the processed user data to obtain the calculated user data, including: counting the number of times each user speaks within a preset time period based on the processed user data; calculating a score for the number of speaking times according to a preset frequency rule to obtain a speaking frequency score; and using the speaking frequency score as the calculated user data.

[0010] Further, the calculation operation is to calculate the speaking duration. Perform a calculation operation on the processed user data to obtain the calculated user data, including: counting the speaking duration of each user based on the processed user data; calculating a score for the speaking duration according to a preset duration rule to obtain a speaking duration score; and using the speaking duration score as the calculated user data.

[0011] Further, the calculation operation is to calculate the number of topics participated in. Perform a calculation operation on the processed user data to obtain the calculated user data, including: invoking natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; counting the number of topics each user participates in for the topic categories in the processed user data; calculating a score for the number of topics participated in according to a preset topic rule to obtain a topic participation number score; and using the topic participation number score as the calculated user data.

[0012] Further, evaluate the activity of group members based on the calculated user data, including: determining the weight value corresponding to the calculated user data according to different group categories; and evaluating the activity of group members based on the calculated user data and the weight value.

[0013] Further, the preprocessing operation is voice conversion. Perform a preprocessing operation on the social data to obtain the processed user data, including: obtaining the voice data in the social data; performing voice conversion on the voice data to obtain user text data; and using the user text data as the processed user data.

[0014] Further, the preprocessing operation is emoji quantization. Perform a preprocessing operation on the social data to obtain the processed user data, including: obtaining the emoji data in the social data; performing emoji quantization on the emoji data to obtain emoji weight information; and using the emoji weight information as the processed user data.

[0015] To achieve the above object, according to the second aspect of the present application, a user activity evaluation device is provided. The device includes: an acquisition unit for acquiring social data of group members; a processing unit for performing preprocessing operations on the social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, speech conversion, and expression quantification; a calculation unit for performing calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculating the speech frequency, calculating the speech duration, and calculating the number of topics participated; an evaluation unit for evaluating the activity of group members based on the calculated user data.

[0016] Further, the calculation operation is to calculate the speech frequency. The calculation unit includes: a first statistics module for statistically counting the number of speeches of each user within a preset duration based on the processed user data; a first calculation module for performing a scoring calculation on the number of speeches according to a preset frequency rule to obtain a speech frequency score; a first determination module for using the speech frequency score as the calculated user data.

[0017] Further, the calculation operation is to calculate the speech duration. The calculation unit includes: a second statistics module for statistically counting the speech duration of each user based on the processed user data; a second calculation module for performing a scoring calculation on the speech duration according to a preset duration rule to obtain a speech duration score; a second determination module for using the speech duration score as the calculated user data.

[0018] Further, the calculation operation is to calculate the number of topics participated. The calculation unit includes: a third determination module for invoking natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; a third statistics module for statistically counting the number of topics participated by each user in the processed user data for the topic categories; a third calculation module for performing a scoring calculation on the number of topics participated according to a preset topic rule to obtain a topic participation number score; a fourth determination module for using the topic participation number score as the calculated user data.

[0019] Further, the evaluation unit includes: a fifth determination module for determining the weight value corresponding to the calculated user data according to different group categories; an evaluation module for evaluating the activity of group members based on the calculated user data and the weight value.

[0020] Further, the preprocessing operation is speech conversion. The processing unit includes: a first acquisition module for acquiring voice data in the social data; a conversion module for performing speech conversion on the voice data to obtain user text data; a sixth determination module for using the user text data as the processed user data.

[0021] Further, the preprocessing operation is expression quantization, and the processing unit includes: a second acquisition module for acquiring emoji data in social data; a quantization module for performing expression quantization on the emoji data to obtain expression weight information; and a seventh determination module for using the expression weight information as the processed user data.

[0022] According to a third aspect of the embodiments of the present application, an electronic device is provided, including: a processor; and a memory, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor, the user activity evaluation method of any one of the above is implemented.

[0023] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the user activity evaluation method according to any one of the above is implemented.

[0024] Through the present application, the following steps are adopted: acquiring social data of group members; performing a preprocessing operation on the social data to obtain processed user data, where the preprocessing operation includes at least one of the following: data cleaning, voice conversion, expression quantization; performing a calculation operation on the processed user data to obtain calculated user data, where the calculation operation includes at least one of the following: calculating the speech frequency, calculating the speech duration, calculating the number of participated topics; and evaluating the activity of group members based on the calculated user data. Through the present application, the problem in the related art that the interaction quality of group members decreases and the group value weakens due to the failure to comprehensively evaluate the group social activity is solved. By calculating the speech frequency, calculating the speech duration, calculating the number of participated topics in multiple dimensions, and evaluating the activity of group members based on the calculated user data, the effect of improving the interaction quality of group members and enhancing the group value is achieved. Description of the Drawings

[0025] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0026] Figure 1 is a flowchart of the user activity evaluation method provided by the embodiments of the present application;

[0027] Figure 2 is a schematic diagram of the speech frequency statistics provided by the embodiments of the present application;

[0028] Figure 3 is a schematic diagram of the speech duration score provided by the embodiments of the present application;

[0029] Figure 4It is a schematic diagram of the participation topic score provided according to an embodiment of the present application;

[0030] Figure 5 It is a schematic diagram of the group member activity statistics provided according to an embodiment of the present application;

[0031] Figure 6 It is a schematic diagram of the activity analysis provided according to an embodiment of the present application;

[0032] Figure 7 It is a schematic diagram of the user activity evaluation device provided according to an embodiment of the present application;

[0033] Figure 8 It is a schematic diagram of the network architecture of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0034] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0035] In order to enable those skilled in the art to better understand the solution of the present application, 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. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without making creative efforts shall fall within the protection scope of the present application.

[0036] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to describe the embodiments of the present application here. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or devices.

[0037] According to an embodiment of the present application, a user activity evaluation method is provided.

[0038] Figure 1 It is a flowchart of the user activity evaluation method according to an embodiment of the present application. As Figure 1 shown, the method includes the following steps:

[0039] Step S101, obtain the social data of group members.

[0040] Among them, obtain the relevant data of each member in a certain group on social media. Here, "social data" usually refers to various information generated by users on social platforms, including but not limited to user profiles (such as name, avatar, geographical location, etc.), published content (such as posts, photos, videos, etc.), interaction behaviors (such as likes, comments, shares, forwards, etc.), and interpersonal relationships (such as friends, followers, fans, etc.).

[0041] Step S102, perform preprocessing operations on the social data to obtain processed user data. Among them, the preprocessing operations include at least one of the following: data cleaning, speech conversion, and emoji quantization.

[0042] Specifically, for group social data, data preprocessing should be carried out first to ensure the standardization of data content and meet the subsequent data weight quantization. The data preprocessing operations in this case can include data cleaning, speech conversion, and emoji quantization. The user data obtained after preprocessing the social data can include user basic profile information, behavior data, and relationship data.

[0043] Among them, data cleaning mainly includes: removing duplicate data. For example, if a user sends the exact same text message "Okay" multiple times in a row, only keep one of them. At the same time, screen out obviously invalid or abnormal data, such as garbled characters, unrecognizable characters, etc., to ensure that the remaining data is valid.

[0044] Specifically, the preprocessing operation is speech conversion. To perform preprocessing operations on the social data and obtain processed user data, it can be achieved through the following steps: obtain the speech data in the social data; perform speech conversion on the speech data to obtain user text data; use the user text data as the processed user data.

[0045] Exemplarily, convert voice messages into text form for unified subsequent processing. For example, the voice "The weather is nice today" sent by a user is converted into the corresponding text through speech recognition technology.

[0046] Specifically, the preprocessing operation is emoji quantization. To perform preprocessing operations on the social data and obtain processed user data, it can be achieved through the following steps: obtain the emoji data in the social data; perform emoji quantization on the emoji data to obtain emoji weight information; use the emoji weight information as the processed user data.

[0047] Exemplarily, perform quantization processing on common emojis and assign them certain numerical values or weights. For example represents a positive attitude and is counted as 1 point; "" represents a negative attitude and is counted as -1 point.

[0048] Through the preprocessing operation of the data, this application can not only improve the quality and standardization of the data, but also expand the types and sources of the data. Furthermore, it can understand the social characteristics and rules of the group more comprehensively and deeply, and provide strong data support for the subsequent quantitative analysis of data weights.

[0049] Step S103: Perform a calculation operation on the processed user data to obtain the calculated user data, where the calculation operation includes at least one of the following: calculating the speech frequency, calculating the speech duration, and calculating the number of topics participated in.

[0050] Specifically, after the data processing is completed, the speech frequency, speech duration, and number of topics of the processed user data can be calculated respectively to obtain the calculated user data. The calculated user data includes: speech frequency score, speech duration score, and number of topic participations score.

[0051] Specifically, the calculation operation is to calculate the speech frequency. Perform a calculation operation on the processed user data to obtain the calculated user data, including: counting the number of speeches of each user within a preset duration based on the processed user data; performing a scoring calculation on the number of speeches according to the preset frequency rule to obtain the speech frequency score; using the speech frequency score as the calculated user data.

[0052] Exemplarily, divide the time period in hours as the preset duration of this application, and count the number of speeches of each user within each time period. For example, as Figure 2 shown, in a work group, user A speaks 3 times between 9 am and 10 am on Monday, 2 times between 10 am and 11 am, and 4 times between 10 am and 11 am on Tuesday, and does not speak in other time periods. Then the speech frequency of user A within a week is 3 + 2 + 4 = 9 times. Assuming that the full score of the speech frequency is 100 points, according to the preset frequency rule, such as 0 points for a speech frequency less than 5 times, 60 points for 5 - 10 times, 80 points for 10 - 15 times, and 100 points for more than 15 times, then the speech frequency score of user A can be calculated as 60 points.

[0053] This application can obtain the speech frequency score by counting the number of speeches and performing a scoring calculation, which helps to quantify the user's activity, evaluate the participation degree, discover potential leaders, and adjust the communication strategy.

[0054] Specifically, the calculation operation is to calculate the speech duration. Perform a calculation operation on the processed user data to obtain the calculated user data, which can be achieved through the following steps: counting the speech duration of each user based on the processed user data; performing a scoring calculation on the speech duration according to the preset duration rule to obtain the speech duration score; using the speech duration score as the calculated user data.

[0055] Exemplarily, in the present application, the start time and end time of each speech are accurately recorded, and the speech duration is obtained by calculating the time difference. Specifically, as Figure 3 shown, for the case of continuous speeches with a short interval (such as the interval not exceeding 5 minutes), it is regarded as one continuous speech for calculating the duration. Suppose a speech of user A starts at 9:10 and ends at 9:15 on Monday morning, with a duration of 5 minutes; another speech starts at 9:20 and ends at 9:23. Since the interval does not exceed 5 minutes, the combined duration is calculated as 13 minutes. The total speech duration of user A in a week is 120 minutes. Suppose the full score of the speech duration is 100 points. According to the preset duration rule, if the speech duration is less than 30 minutes, it is 0 points; 30 - 60 minutes is 60 points; 60 - 90 minutes is 80 points; and more than 90 minutes is 100 points. Then the speech duration score of user A can be calculated as 100 points.

[0056] By statistically calculating the speech duration and performing scoring calculations, and taking it as one of the calculated user data, the present application has the advantages in multiple aspects such as deeply quantifying user participation, reflecting user contribution value, optimizing user management and motivation, improving data analysis accuracy, and promoting the healthy development of the group.

[0057] Specifically, the calculation operation is to calculate the number of topics participated in. The calculated user data is obtained by performing the calculation operation on the processed user data, and can be obtained through the following steps: invoking natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; counting the number of topics participated in by each user in the processed user data for the topic categories; performing scoring calculations on the number of topics participated in according to the preset topic rules to obtain the score for the number of topics participated in; and taking the score for the number of topics participated in as the calculated user data.

[0058] Exemplarily, by using natural language processing technology and semantic analysis algorithms, the group chat content is segmented, keywords are extracted, and semantic understanding is performed. Different topics are divided according to the similarity of keywords and semantics. For example, "project progress", "project advancement", and "project completion status" can be classified into one topic about the project, and "activity arrangement", "activity process", and "activity time" can be classified into one topic about the activity. Count the number of different topics participated in by each user. As Figure 4 shown, suppose user A participated in 5 different topic discussions, and the full score for the number of topics participated in is 100 points. The preset topic rule is set as 0 points if the number of topics participated in is less than 2, and 20 points are added for each additional topic over 2, with a maximum of 100 points. Then the score for the number of topics participated in by user A can be calculated as 60 points.

[0059] This application calculates the score of the topic participation quantity according to the preset topic rules, obtains the topic participation quantity score, which helps to quantify the topic activity, identify popular topics, optimize topic management, promote user communication, and thus enhances the group value.

[0060] Step S104, evaluate the activity of group members based on the calculated user data.

[0061] More specifically, evaluating the activity of group members based on the calculated user data can be obtained through the following steps: determine the weight value corresponding to the calculated user data according to different group categories; evaluate the activity of group members based on the calculated user data and the weight value.

[0062] Specifically, after calculating the scores of each dimension, it is necessary to summarize and count according to different weights. Through the analysis and experiment of a large amount of group social data, determine the weights of the speech frequency, speech duration, and the number of participated topics.

[0063] Exemplarily, assume that after in-depth research on 1000 group chats, it is determined that the weight of the speech frequency is 0.3, the weight of the speech duration is 0.4, and the weight of the number of participated topics is 0.3. At the same time, make appropriate adjustments according to the types and characteristics of different groups. For the work communication group, the weight of the speech duration may be increased to 0.5; for the interest group, the weight of the number of participated topics may be increased to 0.4, and finally calculate the activity score of each group member. The following is the activity score calculation formula:

[0064] Activity score = speech frequency score × speech frequency weight + speech duration score × speech duration weight + number of participated topics score × number of participated topics weight.

[0065] Exemplarily, for the above user A (speech frequency score 60 points, weight 0.3) (speech duration score 100 points, weight 0.4) (number of participated topics score 60 points, weight 0.3), assuming the weights are set as above, their activity scores are calculated as follows: User A: 60×0.3 + 100×0.4 + 60×0.3 = 76 points. Then summarize and display all group social members to obtain the group member activity analysis table, where the activity analysis details are as follows Figure 5 shown.

[0066] Optionally, as Figure 6As shown in the figure, for group social data, this application first needs to perform data preprocessing to ensure the standardization of data content, which meets the subsequent data weight quantification. Specifically, it includes three aspects: data cleaning, voice conversion, and expression quantification. After the data processing is completed, the speech frequency, speech duration, and the number of topics participated in are calculated respectively. That is, this application improves the overall activity and cohesion of the group, and promotes the communication and interaction among users through multi-factor comprehensive evaluation, scientific weight distribution algorithm, accurate data processing and analysis methods, and efficient calculation processes.

[0067] In summary, the user activity evaluation method provided by the embodiments of this application includes: obtaining the social data of group members; performing preprocessing operations on the social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, voice conversion, and expression quantification; performing calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculating speech frequency, calculating speech duration, and calculating the number of topics participated in; and evaluating the activity of group members based on the calculated user data. Through this application, the problem in the related art that the interaction quality of group members decreases and the group value weakens due to the failure to comprehensively evaluate the group social activity is solved. By calculating the speech frequency, speech duration, and the number of topics participated in in multiple dimensions, and evaluating the activity of group members based on the calculated user data, the effect of improving the interaction quality of group members and enhancing the group value is achieved.

[0068] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0069] The embodiments of this application also provide a user activity evaluation device. It should be noted that the user activity evaluation device of the embodiments of this application can be used to execute the user activity evaluation method provided by the embodiments of this application. The following introduces the user activity evaluation device provided by the embodiments of this application.

[0070] Figure 7 is a schematic diagram of the user activity evaluation device 700 according to the embodiments of this application. As Figure 7 shown, the device includes: an acquisition unit 701, a processing unit 702, a calculation unit 703, and an evaluation unit 704.

[0071] Specifically, the acquisition unit 701 is configured to acquire the social data of group members;

[0072] A processing unit 702, configured to perform preprocessing operations on social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, speech conversion, and expression quantization;

[0073] A calculation unit 703, configured to perform calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculating the speaking frequency, calculating the speaking duration, and calculating the number of topics participated in;

[0074] An evaluation unit 704, configured to evaluate the activity of group members based on the calculated user data.

[0075] In summary, for the user activity evaluation device provided in the embodiments of the present application, an acquisition unit 701 is used to acquire social data of group members; a processing unit 702 is used to perform preprocessing operations on the social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, speech conversion, and expression quantization; a calculation unit 703 is used to perform calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculating the speaking frequency, calculating the speaking duration, and calculating the number of topics participated in; an evaluation unit 704 is used to evaluate the activity of group members based on the calculated user data, which solves the problem in the related art that due to the failure to comprehensively evaluate the group social activity, the interaction quality of group members decreases and the group value weakens. By calculating the speaking frequency, speaking duration, and number of topics participated in from multiple dimensions, and evaluating the activity of group members based on the calculated user data, the effect of improving the interaction quality of group members and enhancing the group value is achieved.

[0076] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the calculation operation is to calculate the speaking frequency, and the calculation unit includes: a first statistics module, configured to count the number of times each user speaks within a preset duration based on the processed user data; a first calculation module, configured to perform score calculation on the number of speaking times according to a preset frequency rule to obtain a speaking frequency score; a first determination module, configured to use the speaking frequency score as the calculated user data.

[0077] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the calculation operation is to calculate the speaking duration, and the calculation unit includes: a second statistics module, configured to count the speaking duration of each user based on the processed user data; a second calculation module, configured to perform score calculation on the speaking duration according to a preset duration rule to obtain a speaking duration score; a second determination module, configured to use the speaking duration score as the calculated user data.

[0078] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the calculation operation is to calculate the number of topics participated in. The calculation unit includes: a third determination module, configured to call natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; a third statistics module, configured to count the number of topic participations of each user in the processed user data for each topic category; a third calculation module, configured to perform a scoring calculation on the number of topic participations according to a preset topic rule to obtain a score for the number of topic participations; a fourth determination module, configured to use the score for the number of topic participations as the processed user data.

[0079] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the evaluation unit includes: a fifth determination module, configured to determine the weight value corresponding to the processed user data according to different group categories; an evaluation module, configured to evaluate the activity of group members based on the processed user data and the weight value.

[0080] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the preprocessing operation is voice conversion. The processing unit includes: a first acquisition module, configured to acquire voice data in social data; a conversion module, configured to perform voice conversion on the voice data to obtain user text data; a sixth determination module, configured to use the user text data as the processed user data.

[0081] Optionally, in the user activity evaluation device provided in the embodiments of the present application, the preprocessing operation is emoji quantization. The processing unit includes: a second acquisition module, configured to acquire emoji data in social data; a quantization module, configured to perform emoji quantization on the emoji data to obtain emoji weight information; a seventh determination module, configured to use the emoji weight information as the processed user data.

[0082] The user activity evaluation device includes a processor and a memory. The above-mentioned acquisition unit 701, processing unit 702, calculation unit 703, evaluation unit 704, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

[0083] The processor contains a kernel, and the kernel retrieves the corresponding program unit from the memory. One or more kernels can be set, and the user activity is evaluated by adjusting the kernel parameters.

[0084] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of, for example, read-only memory (ROM) or flash memory (flash RAM). The memory includes at least one storage chip.

[0085] In an exemplary embodiment of the present application, a computer storage medium capable of implementing the above method is further provided. A program product capable of implementing the above method of this specification is stored thereon. In some possible embodiments, various aspects of the present application can also be implemented in the form of a program product, which includes program code. When the program product runs on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary embodiments of the present application described in the above "Exemplary Method" section of this specification. For example, the following steps can be executed: obtaining social data of group members; performing preprocessing operations on the social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, speech conversion, and expression quantification; performing calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculating the speaking frequency, calculating the speaking duration, and calculating the number of topics participated in; evaluating the activity of group members based on the calculated user data.

[0086] In an alternative embodiment: counting the number of times each user speaks within a preset duration based on the processed user data; performing a scoring calculation on the number of speaking times according to a preset frequency rule to obtain a speaking frequency score; using the speaking frequency score as the calculated user data.

[0087] In an alternative embodiment: counting the speaking duration of each user based on the processed user data; performing a scoring calculation on the speaking duration according to a preset duration rule to obtain a speaking duration score; using the speaking duration score as the calculated user data.

[0088] In an alternative embodiment: invoking natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; counting the number of topics each user in the processed user data participates in for the topic categories; performing a scoring calculation on the number of topic participations according to a preset topic rule to obtain a topic participation number score; using the topic participation number score as the calculated user data.

[0089] In an alternative embodiment: determining a weight value corresponding to the calculated user data according to different group categories; evaluating the activity of group members based on the calculated user data and the weight value.

[0090] In an alternative embodiment: obtaining voice data in the social data; performing voice conversion on the voice data to obtain user text data; using the user text data as the processed user data.

[0091] In an alternative embodiment: obtaining emoji data in the social data; performing emoji quantification on the emoji data to obtain emoji weight information; using the emoji weight information as the processed user data.

[0092] In an alternative embodiment, the embodiment of the present application may further include a program product for implementing the above method. The program product may be a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium may be any tangible medium that contains or stores a program, and the program may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0093] The program product may employ any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage medium may, for example, but not be limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0094] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which the readable program code is carried. Such a propagated data signal may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium may also be any readable medium other than the readable storage medium, and the readable medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0095] The program code contained on the readable medium may be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the above.

[0096] The program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and also including conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0097] In addition, in an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0098] Those skilled in the art can understand that various aspects of the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "system" here.

[0099] The following refers to Figure 8 to describe the electronic device 800 according to this embodiment of the present application. Figure 8 The electronic device 800 shown is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0100] As Figure 8 shown, the electronic device 800 is presented in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one of the above-mentioned processing units 810, at least one of the above-mentioned storage units 820, a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810), and a display unit 840.

[0101] Among them, the storage unit stores program codes, which can be executed by the processing unit 810, so that the processing unit 810 executes the steps according to various exemplary embodiments of the present application described in the "Exemplary Method" section above of this specification. For example, the processing unit 810 can execute the following steps: obtain the social data of group members; perform preprocessing operations on the social data to obtain processed user data, where the preprocessing operations include at least one of the following: data cleaning, speech conversion, and expression quantization; perform calculation operations on the processed user data to obtain calculated user data, where the calculation operations include at least one of the following: calculate the speech frequency, calculate the speech duration, calculate the number of topics participated; evaluate the activity of group members based on the calculated user data.

[0102] In an alternative embodiment: count the number of speeches of each user within a preset duration based on the processed user data; perform a scoring calculation on the number of speeches according to a preset frequency rule to obtain a speech frequency score; use the speech frequency score as the calculated user data.

[0103] In an alternative embodiment: count the speech duration of each user based on the processed user data; perform a scoring calculation on the speech duration according to a preset duration rule to obtain a speech duration score; use the speech duration score as the calculated user data.

[0104] In an alternative embodiment: call natural language processing technology and semantic analysis technology to determine the topic categories in the processed user data; count the number of topic participations of each user in the processed user data for the topic categories; perform a scoring calculation on the number of topic participations according to a preset topic rule to obtain a topic participation number score; use the topic participation number score as the calculated user data.

[0105] In an alternative embodiment: determine the weight value corresponding to the calculated user data according to different group categories; evaluate the activity of group members based on the calculated user data and the weight value.

[0106] In an alternative embodiment: obtain the voice data in the social data; perform voice conversion on the voice data to obtain user text data; use the user text data as the processed user data.

[0107] In an alternative embodiment: obtain the emoji data in the social data; perform expression quantization on the emoji data to obtain expression weight information; use the expression weight information as the processed user data.

[0108] The storage unit 820 may include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 8201 and / or a cache storage unit 8202, and may further include a read-only storage unit (ROM) 8203.

[0109] The storage unit 820 may also include a program / utilities 8204 having a set (at least one) of program modules 8205. Such program modules 8205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment.

[0110] The bus 830 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus structures.

[0111] The electronic device 800 may also communicate with one or more external devices 900 (such as a keyboard, a pointing device, a Bluetooth device, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 800, and / or may communicate with any device that enables the electronic device 800 to communicate with one or more other computing devices (such as a router, a modem, etc.). Such communication may be through an input / output (I / O) interface 850. Moreover, the electronic device 800 may also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 860. As shown in the figure, the network adapter 860 communicates with other modules of the electronic device 800 through the bus 830. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0112] Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein can be implemented by software, or can be implemented by a combination of software and necessary hardware. Therefore, the technical solutions according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0113] In addition, the above-mentioned drawings are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, rather than for limiting purposes. It is easy to understand that the processes shown in the above-mentioned drawings do not indicate or limit the chronological order of these processes. Additionally, it is also easy to understand that these processes can be executed synchronously or asynchronously in, for example, multiple modules.

[0114] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any variations, uses, or adaptations of the present application, which follow the general principles of the present application and include known common knowledge or conventional technical means in the technical field not disclosed in the present application. The specification and embodiments are only regarded as exemplary, and the true scope and spirit of the present application are pointed out by the claims.

Claims

1. A method for evaluating user activity, characterized in that: include: Get social data of group members; Performing a preprocessing operation on the social data to obtain processed user data, wherein the preprocessing operation includes at least one of the following: data cleaning, voice conversion, and expression quantification; Performing a calculation operation on the processed user data to obtain calculated user data, wherein the calculation operation includes at least one of the following: calculating the speaking frequency, calculating the speaking duration, and calculating the number of participating topics; The activity of the group members is evaluated based on the calculated user data.

2. The method according to claim 1, characterized in that The calculation operation is to calculate the speaking frequency, and the calculation operation is performed on the processed user data to obtain the calculated user data, including: Counting the number of times each user speaks within a preset time period based on the processed user data; Score and calculate the number of speeches according to a preset frequency rule to obtain a speech frequency score; The speech frequency score is used as the calculated user data.

3. The method according to claim 1, characterized in that The calculation operation is to calculate the speaking time, and the processed user data is subjected to the calculation operation to obtain the calculated user data, including: Counting the speaking time of each user based on the processed user data; Score and calculate the speaking time according to the preset time rule to obtain a speaking time score; The speaking time score is used as the calculated user data.

4. The method according to claim 1, characterized in that: The calculation operation is to calculate the number of participating topics, and the processed user data is subjected to a calculation operation to obtain the calculated user data, including: Invoking natural language processing technology and semantic analysis technology to determine the topic category in the processed user data; Counting the number of topic participations of each user in the processed user data for the topic category; Score and calculate the number of participants in the topic according to the preset topic rules to obtain a topic participation number score; The topic participation quantity score is used as the calculated user data.

5. The method according to claim 1, characterized in that Evaluating the activity of the group members based on the calculated user data includes: Determining weight values ​​corresponding to the calculated user data according to different group categories; The activity of the group members is evaluated based on the calculated user data and the weight value.

6. The method according to claim 1, characterized in that The preprocessing operation is the voice conversion, and the social data is preprocessed to obtain processed user data, including: Acquire voice data from the social data; Performing voice conversion on the voice data to obtain user text data; The user text data is used as the processed user data.

7. The method according to claim 1, characterized in that The preprocessing operation is the expression quantization, and the social data is preprocessed to obtain processed user data, including: Obtaining emoticon data in the social data; Performing expression quantization on the emoticon data to obtain expression weight information; The expression weight information is used as the processed user data.

8. A user activity evaluation device, characterized in that: include: An acquisition unit, used to acquire social data of group members; A processing unit, configured to perform a preprocessing operation on the social data to obtain processed user data, wherein the preprocessing operation includes at least one of the following: data cleaning, voice conversion, and expression quantization; A calculation unit, configured to perform a calculation operation on the processed user data to obtain calculated user data, wherein the calculation operation includes at least one of the following: calculating a speaking frequency, calculating a speaking duration, and calculating a number of participating topics; An evaluation unit is used to evaluate the activity of the group members based on the calculated user data.

9. A computer-readable storage medium, characterized in that: The storage medium includes a stored program, wherein the program executes the user activity evaluation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors, a memory, a display device, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing the user activity evaluation method described in any one of claims 1 to 7.

Images