CN118710319B - Method and system for analyzing bid - Google Patents
Method and system for analyzing bidInfo
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- CN118710319B CN118710319B CN202410929704.0A CN202410929704A CN118710319B CN 118710319 B CN118710319 B CN 118710319B CN 202410929704 A CN202410929704 A CN 202410929704A CN 118710319 B CN118710319 B CN 118710319B
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Abstract
本发明涉及竞品分析技术领域，尤其涉及竞品分析的方法及系统。本发明通过关键词信息，基于社交媒体账号发表作品、评论、声量数据，并进行投放分析、内容分析、传播分析，通过采集海量的数据分析出与关键词匹配的数据信息，从匹配的数据整理成分析报告实现竞品分析。该系统包括竞品分析任务创建和维护单元、社交媒体账号发表作品、评论、声量数据的采集存储单元、竞品分析报告的分类和展示单元。本发明有益效果为对社交媒体账号数据进行竞品分析技术全面使企业做战略定位和产品优化更细致。
The present invention relates to the field of competitive product analysis technology, and in particular to a method and system for competitive product analysis. The present invention uses keyword information, based on social media accounts to publish works, comments, and voice data, and conducts delivery analysis, content analysis, and dissemination analysis. By collecting massive amounts of data to analyze data information that matches keywords, the matching data is organized into analysis reports to achieve competitive product analysis. The system includes a competitive product analysis task creation and maintenance unit, a social media account publication work, comment, and voice data collection and storage unit, and a competitive product analysis report classification and display unit. The beneficial effect of the present invention is that the competitive product analysis technology for social media account data is comprehensive, enabling enterprises to make strategic positioning and product optimization more detailed.
Description
Technical Field
The invention relates to the technical field of bid analysis, in particular to a method and a system for bid analysis.
Background
With the development of social media, more and more enterprises can cover various fields and topics by creating and publishing contents in the form of individuals or teams, and the convenience and instantaneity make the social media an important platform for the enterprises to conduct marketing, brand popularization, customer service and other activities. Enterprises can interact with users directly through social media to know their demands and feedback, and meanwhile, the enterprises can accurately attract target audiences by means of advertising and popularization functions of the social media.
In the development of enterprises, the enterprises continuously push out and improve products to occupy the market, and the bid product analysis is an important means for helping to improve the products of the enterprises in the market, and the promotion works of competitors on social media are observed to know the characteristics, the functional advantages and the like of the products, so that the advantages and the disadvantages of the products are compared, and references are provided for product improvement and market positioning.
User comments of the promoted works can reflect real demands and feedback of users, and comments and feedback of the users on the bidding products on social media are analyzed, so that attitudes and satisfaction of the users can be known more accurately. The comment data can help enterprises to find out problems of the bid in time, improve products or services of the enterprises in a targeted manner, and improve user experience and satisfaction.
The bid analysis on social media can provide rich market information and user feedback for enterprises, help the enterprises to better understand market dynamics, optimize products and services, and formulate effective marketing strategies, so that user loyalty is increased, and brand competitiveness is improved.
Chinese patent publication No. CN105608593a discloses monitoring and responding to social media posts with a social related comparison, monitoring, by a computing device comprising at least one processor, a plurality of social media posts for one or more keywords referencing related to a brand, determining, by the computing device, at least one topic mentioned in the plurality of social media posts related to the brand, identifying, by the computing device, whether the plurality of social media posts have a positive emotion or a negative emotion in terms of the at least one topic, and upon identifying a social media post having a negative emotion in terms of the at least one topic concerned, replying to the social media post having a negative emotion with a response based on at least one social media post from the plurality of social media posts having a positive emotion in terms of the at least one topic.
It can be seen that the prior art has the following problems that enterprises do strategic positioning and product optimization are not careful due to the fact that the bid analysis technology is not comprehensive on social media account data.
Disclosure of Invention
Therefore, the invention provides a method and a system for analyzing the bid, which are used for solving the problems that in the prior art, enterprises do strategic positioning and product optimization is not careful due to incomplete bid analysis technology on social media account data.
In order to achieve the above purpose, the invention provides a bid analysis method, which screens out the putting account number, putting content and spreading condition of the bid based on the works, comments and sound volume data issued by the social media account number, and analyzes the product characteristics, function advantages and disadvantages of the bid and the real demands and feedback of users;
Creating an analysis task, and determining keywords of products and related bid products, wherein the keywords do not contain keywords and time ranges of the products;
collecting works, comments, sound volume, fan and interactive user data of a social media account, and storing the works, comments, sound volume, fan and interactive user data through a storage medium;
Analyzing the acquired data stored in the storage medium by using a bid analysis tool package, performing data analysis according to three dimensions of delivery analysis, content analysis and propagation analysis, and storing the analyzed data through the storage medium;
displaying analysis results according to the three dimensions of the release analysis, the content analysis and the propagation analysis, and formulating marketing and popularization strategies according to the analysis results to predict industry development trend;
Determining bid scores according to bid video praise numbers, forwarding times and comment times;
Setting a compensation parameter of the first-level flow praise number for scoring the bid, a compensation parameter of the second-level flow praise number for scoring the bid, the first-level flow equal praise number, the second-level flow equal praise number and the second-level flow equal praise number;
Determining influence supplementary parameters of the number of praise to the bid scoring according to the comparison result of the preset parameters and the actual praise, and calculating the corresponding bid scoring;
Judging whether to bring the bid into a detection range or not based on the comparison result of the bid score and a preset bid score;
Setting bid scoring as Wherein n is a bid video number, i=1, 2,..n, di is a bid i video number, K1 is a bid i video number impact compensation parameter, K2 is a bid i video number impact compensation parameter, pi is a bid i video number of comments, and K3 is a bid number impact compensation parameter for the bid score;
setting K1=K10+K11, wherein K10 is a compensation parameter related to the number of first-level flow praise and bid scores, and K11 is a compensation parameter related to the number of second-level flow praise and bid scores;
The system sets the first-level flow equal praise number Dz1, the first-level flow equal praise number Dz2, the second-level flow equal praise number Di 1, the second-level flow equal praise number Di2,
In this embodiment, if 0 is less than or equal to Di < Dz1, k10=k101, where K101 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number 0 is less than or equal to D101< Dz1, and d101=0.2 is set;
If dz1 is less than or equal to Di is less than or equal to dz2, k10=k102, wherein K102 is an influence supplementary parameter of the first-order flow praise number on the bid grade when praise number dz1 is less than or equal to Di is less than or equal to dz2, and d102=0.5 is set;
if Di > Dz2, k10=k103, where K103 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number Di > Dz2, and d103=0.8 is set;
if 0 is less than or equal to Di < Di 1, k11=k111, where K111 is an influence supplementary parameter of the secondary flow praise number on the bid score when the praise number 0 is less than or equal to Di < Di 1, and d111=0.3 is set;
If Di 1< Di2, k11=k112, where K112 is an influence supplementary parameter of the second-level flow praise number on the bid score when praise number Di 1< Di2, d112=0.6 is set;
If Di > Di2, k11=k113, where K113 is an influence supplementary parameter of the secondary flow praise number on the bid score when praise number Di > Di2, and d113=0.9 is set;
The system is provided with a first bid score F1, a second bid score F2,
If F < F1, the bid is a third bid, and the monitoring range is not included;
If F1 is not less than F2, the bid product is a second bid product and is included in the monitoring range;
if F > F2, the bid is a first bid, and the bid is included in the monitoring range and analyzed.
Further, a task management system is established and used for planning, executing and monitoring a bid analysis flow, determining keywords of a bid analysis task according to the product profile and the product nickname, and generating task fields according to the keywords, wherein the task fields comprise unique IDs, task states, analysis time, keywords and analysis dimensions.
Further, the work data, comment data, sound volume data and interaction data of the account are obtained through a data mining technology and an open application programming interface technology and stored through a storage medium to be used as a data source for bid analysis;
The acquired data are subjected to data arrangement every day, the data are acquired and maintained for a long time at regular time, the acquired data comprise account information, work information, comment information, sound volume information and interaction information, and corresponding fields are generated, and the corresponding fields comprise unique ID, acquisition content, acquisition type and acquisition time
Further, the collected data is subjected to multidimensional analysis according to the keywords and the time range of the task, including,
The method comprises the steps of putting analysis, namely analyzing account advertisement putting conditions of the bid on social media, wherein an analysis result comprises an putting channel, putting time, a putting form, a putting account, a putting quantity and an operation main body of the account;
Content analysis, namely analyzing work information which is released by a released account and accords with task keywords and comment information corresponding to the work, extracting paragraphs of work information keys by using a natural language processing technology, classifying the key paragraphs and the comment information according to analysis tags, and performing emotion analysis on the key paragraphs and the comment information, wherein analysis results comprise the number of the works, the types and the duty ratio of the works, the analysis tags, the corresponding work paragraphs, the work paragraphs and the corresponding comment emotion analysis and duty ratio;
The method comprises the steps of propagation analysis, analysis of sound volume data and interaction user information, wherein the sound volume data accords with task keyword works, the sound volume data comprises reading, commenting, sharing, forwarding, coin inserting and interaction data, the interaction user information comprises geographic positions, age groups and sexes of users, and analysis results comprise TOP values, average values, interaction user area distribution, age distribution and fan portraits of sound volumes.
Further, the task is divided into a plurality of subtasks through the dimension, the subtask is analyzed in parallel by adopting a distributed technology, and a subtask field is generated, wherein the subtask field comprises a subtask unique ID, an analysis task unique ID, a state and time, the state is updated after the subtask analysis is completed, and the analysis task checks the subtask analysis progress through the unique ID associated with the subtask.
Further, when the analysis task is carried out, the analysis result can be stored and updated through a storage medium, and a task result field is generated, wherein the field comprises a task result unique ID, an analysis task unique ID, an analysis result and storage time.
The invention also provides a bid analysis system, which comprises,
The task management module is used for managing and monitoring the whole bid analysis flow, including task creation, distribution, state tracking, reminding and notification;
The data acquisition module acquires work data, comment data, sound volume data and interaction data of a related bid item selection input account number based on the social media platform, acquires related data from the target social media platform regularly or in real time by adopting a data capture tool or an application programming interface, stores and processes the related data, and provides data support for subsequent bid item analysis;
the bid analysis module is used for carrying out analysis tasks on the collected data according to three dimensions of delivery analysis, content analysis and propagation analysis, adopting a distributed technology for parallel analysis, creating subtask information through a storage medium, maintaining the subtask state, and storing analysis results for data display after analysis is completed;
and the data display module displays the results of the bid analysis to the user, wherein the results comprise reports, charts and data visualization.
The present invention also provides a storage medium having stored therein a plurality of instructions for loading by a processor for performing the steps of the bid analysis method.
The invention also provides an electronic device, which comprises the storage medium and a processor, wherein the processor is used for executing the instructions in the storage medium.
Compared with the prior art, the invention has the beneficial effects that the bid product analysis system relies on the data published by the social media account to carry out data analysis in a multi-dimensional manner according to the delivery analysis, the content analysis and the propagation analysis, the social media account has large user scale, and the popularization of the works and comment data can intuitively help people to know the popularization condition of the bid product, so that the practicability of the invention is improved.
Further, the interaction condition of the bid account on the social media is analyzed, including praise, comment, forwarding, coin-feed and the like, so that the user is helped to know the interaction mode and effect between the bid account and the user, and the social interaction strategy of the user is optimized.
Further, audience characteristics of the bid account, including age, gender, interests and the like, are analyzed, a user is helped to locate a target audience more accurately, content location and marketing strategies of the user are adjusted accordingly, appropriate content and marketing strategies are recommended to the user, preemption is helped, and a competitive advantage is maintained.
Further, the system provides user-friendly data reporting and visualization charts that enable users to easily view and understand data analysis results, quickly make decisions and adjust policies.
Specifically, the bidding products can be screened out and analyzed by scoring the bidding products which are closer to the bidding products, the efficiency of the analysis of the bidding products is improved, and the time waste of the analysis of all the bidding products is avoided.
Specifically, the bid score can effectively capture the next bid according to the number of the bid scores and the number of the bid scores, and the bid analysis is more accurate, wherein the bid score is provided with a primary flow score and a secondary flow score, the primary flow score is natural flow and the number of the bid scores is counted, the secondary flow score is the number of the forwarded videos and the bid scores, the bid popularity can be accurately judged, and the influence on the bid score caused by the lack of the number of the bid scores after the forwarding is avoided, and the bid analysis result is further influenced.
Specifically, the compensation parameters of the bid amount for scoring the bid are divided into the compensation parameters of the primary flow amount for scoring the bid, the compensation parameters of the secondary flow amount for scoring the bid are divided into the compensation parameters of the primary flow amount for scoring the bid, the primary flow amount for scoring is different from the compensation parameters of the secondary flow amount for scoring the bid, the secondary flow amount for scoring is different from the compensation parameters of the secondary flow amount for scoring the bid, the effective analysis can be performed on the bid more accurately, and the situation that the different bid amounts for scoring the bid are identical due to the fact that the different bid amounts for scoring are different is avoided, so that the bid score does not reflect the influence of the difference on the selection and analysis of the bid on the different bid is avoided.
Further, the social media account number bid analysis system provides comprehensive and timely bid information and data support for users, helps the users to better understand the market environment, optimizes own operation strategies and improves competitiveness.
Furthermore, the bid analysis needs to read massive data for analysis, the parallelization and distributed computing technology is adopted, the data and the tasks are divided into small blocks according to the displayed classification for parallel processing, so that the analysis speed and the analysis efficiency are improved, the whole analysis task is divided into modules for independent calculation according to the classification, the modules for independent calculation are reported after the completion of the independent calculation, and the task management modules are uniformly reported after the completion of the calculation modules of the whole task, so that the high cohesion and low coupling of the system are realized, and the expandability and the parallel processing capability of the system are improved.
Drawings
FIG. 1 is a flow chart of a bid analysis method in the present embodiment;
FIG. 2 is a flowchart showing the operation of the bid analysis system according to the present embodiment;
FIG. 3 is a schematic diagram of the electronic device components used for analyzing the bidding products in the present embodiment;
fig. 4 is a flowchart of a process of acquiring data for comparison in the bid analysis method in the present embodiment.
Detailed Description
The invention will be further described with reference to examples for the purpose of making the objects and advantages of the invention more apparent, it being understood that the specific examples described herein are given by way of illustration only and are not intended to be limiting.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
It should be noted that, in the description of the present invention, terms such as "upper," "lower," "left," "right," "inner," "outer," and the like indicate directions or positional relationships based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the apparatus or elements must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In addition, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and for example, can be fixedly connected, can be detachably connected, or integrally connected, can be mechanically connected, can be electrically connected, can be directly connected, can be indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1, a flowchart of a method for analyzing a bid in the present embodiment includes,
Step S100, creating a bid analysis task, determining a bid analysis keyword, not containing the keyword, a time range and an analysis dimension, building a task table related field, managing the task by maintaining a data field, and checking an analysis progress;
Step S200, the bid analysis data is based on the work related data of social media, account data with higher transmission degree and vermicelli quantity in the social media is selected, data monitoring and acquisition are carried out at daily timing, and acquired ranges are the works, comments and popularization conditions of accounts and serve as data sources of bid analysis;
The collected data is subjected to preliminary cleaning, pictures, expressions and hyperlinks in works and comments are identified by using a regular expression or a text processing tool, the pictures, the expressions and the hyperlinks are removed or restored according to requirements, non-text characters such as special characters and punctuation marks are removed, and repeated comments are identified by using a hash algorithm or text similarity calculation;
Generating unique IDs (identity) by the processed account numbers and the work data, binding the work data according to the unique IDs of the account numbers, binding comments and popularization data according to the unique IDs of the work, maintaining the data according to the acquisition time, and comparing and analyzing historical data based on time;
Step S300, analyzing the acquired account data according to task indexes, wherein the analyzed dimensions are the account data selected and put for bidding products, works published by the account, comment data and propagation conditions of the works, and finally generating a data report and recommending an operation strategy based on the generated data report;
the method comprises the steps that a put account analyzes the put position, the put period, the account category, the put quantity and the operation main body of the account;
The works and comment data are analyzed into put works, original works proportion, single image-text proportion, 10 ten thousand+ proportion of reading number, deletion proportion, repeated titles, original text links, sentences related to the bid products in the works, comment word clouds, high-frequency keywords and comment emotion scores;
the work propagation data are analyzed into reading, praying, commenting, watching and interacting of the work, the highest and average data are arranged, and fan-shaped images, age distribution and area distribution are generated based on the data of the participating users;
Step S400, storing the analyzed result based on a storage medium, and binding the result data with the unique ID of the task, so that the inquiry during the data display is facilitated;
And S500, displaying the analysis result of the bid, intuitively displaying various indexes and analysis results of the bid according to the classification standards by using result data in a form and graph mode, wherein each classified data supports an independent downloading function, and can download a complete analysis report.
Specifically, bid scores are set, and the bid scores are classified according to praise, forwarding and comment.
Specifically, to conduct potential analysis on bid data,
Setting bid scoring asWherein n is a bid video number, i=1, 2,..n, di is a bid i video number, K1 is a bid i video number impact compensation parameter, K2 is a bid i video number impact compensation parameter, pi is a bid i video number of comments, and K3 is a bid number impact compensation parameter for the bid score;
Setting K1=K10+K11, wherein K10 is a compensation parameter related to the number of first-level flow praise and bid scores, and K11 is a compensation parameter related to the number of second-level flow praise and bid scores;
the system sets the first-level flow equal praise number Dz1, the first-level flow equal praise number Dz2, the second-level flow equal praise number Di1, the second-level flow equal praise number Di2,
In this embodiment, if 0 is less than or equal to Di < Dz1, k10=k101, where K101 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number 0 is less than or equal to D101< Dz1, and d101=0.2 is set;
If dz1 is less than or equal to Di is less than or equal to dz2, k10=k102, wherein K102 is an influence supplementary parameter of the first-order flow praise number on the bid grade when praise number dz1 is less than or equal to Di is less than or equal to dz2, and d102=0.5 is set;
If Di > Dz2, k10=k103, where K103 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number Di > Dz2, and d103=0.8 is set;
If 0 is less than or equal to Di < Di1, k11=k111, where K111 is an influence supplementary parameter of the secondary flow praise number on the bid score when the praise number 0 is less than or equal to Di < Di1, and d111=0.3 is set;
If Di1< Di2, k11=k112, where K112 is an impact supplementary parameter of the secondary flow praise number on the bid score when praise number Di1< Di2, d112=0.6 is set;
if Di > Di2, k11=k113, where K113 is an influence supplementary parameter of the secondary flow praise number on the bid score when praise number Di > Di2, and d113=0.9 is set;
The system is provided with a first bid score F1, a second bid score F2,
If F < F1, the bid is a third bid, and the monitoring range is not included;
If F1 is not less than F2, the bid product is a second bid product and is included in the monitoring range;
if F > F2, the bid is a first bid, and the bid is included in the monitoring range and analyzed.
The invention has the advantages that the bid product analysis system relies on the data published by the social media account to carry out data analysis in multiple dimensions according to the delivery analysis, the content analysis and the propagation analysis, the social media account has large user scale, and the popularization works and comment data can intuitively help people to know the popularization condition of the bid product, so that the practicability of the invention is improved.
Specifically, the interaction condition of the bid account on the social media is analyzed, including praise, comment, forwarding, coin-feed and the like, so that the user is helped to know the interaction mode and effect between the bid account and the user, and the social interaction strategy of the user is optimized.
Specifically, audience characteristics of the bid account, including age, gender, interests and the like, are analyzed, a user is helped to locate a target audience more accurately, content location and marketing strategies of the user are adjusted accordingly, appropriate content and marketing strategies are recommended to the user, preemption is helped, and a competitive advantage is maintained.
In particular, the system provides user-friendly data reporting and visualization charts that enable users to easily view and understand data analysis results, quickly make decisions and adjust strategies.
Specifically, the bidding products can be screened out and analyzed by scoring the bidding products which are closer to the bidding products, the efficiency of the analysis of the bidding products is improved, and the time waste of the analysis of all the bidding products is avoided.
Specifically, the bid score can effectively capture the next bid according to the number of the bid scores and the number of the bid scores, and the bid analysis is more accurate, wherein the bid score is provided with a primary flow score and a secondary flow score, the primary flow score is natural flow and the number of the bid scores is counted, the secondary flow score is the number of the forwarded videos and the bid scores, the bid popularity can be accurately judged, and the influence on the bid score caused by the lack of the number of the bid scores after the forwarding is avoided, and the bid analysis result is further influenced.
Specifically, the compensation parameters of the bid amount for scoring the bid are divided into the compensation parameters of the primary flow amount for scoring the bid, the compensation parameters of the secondary flow amount for scoring the bid are divided into the compensation parameters of the primary flow amount for scoring the bid, the primary flow amount for scoring is different from the compensation parameters of the secondary flow amount for scoring the bid, the secondary flow amount for scoring is different from the compensation parameters of the secondary flow amount for scoring the bid, the effective analysis can be performed on the bid more accurately, and the situation that the different bid amounts for scoring the bid are identical due to the fact that the different bid amounts for scoring are different is avoided, so that the bid score does not reflect the influence of the difference on the selection and analysis of the bid on the different bid is avoided.
Referring now to fig. 2, a flowchart illustrating the operation of the bid analysis system of the present embodiment includes,
A task management module for creating and managing a bid analysis task, including,
The system comprises a task creating and analyzing unit, a task creating and analyzing unit and a task managing unit, wherein the task creating and analyzing unit is used for setting task names, analysis types, time ranges and analysis dimensions, and after the task creating is completed, the system can generate a unique task ID;
The management analysis task unit is used for checking all analysis tasks, including task names, states, starting time and ending time, and enabling a user to edit, pause, resume, delete and the like;
The data acquisition module is used for acquiring work data, comment data and propagation data of the account through an acquisition technology and an open application programming interface technology;
the work data comprises various contents such as characters, pictures, videos and the like which are released by the account. The main collected information comprises a work ID, release time, a content text, a picture/video link, a praise number, a forwarding number and a comment number, and the content type, release frequency and popularity of an account can be analyzed by collecting work data;
The comment data comprise comment IDs, comment time, comment content, comment IDs and praise numbers, and the feedback and emotion tendencies of the users to the works and interaction conditions among the users can be analyzed through collecting the comment data;
the transmission detail data comprise the forwarding and sharing conditions of the works and the behaviors of related users, the acquired information comprises forwarding/sharer ID, forwarding/sharing time, forwarding/sharing content, forwarding/sharing paths and the like, and the transmission paths, influence ranges and transmission effects of the works can be analyzed by acquiring the transmission detail data;
aiming at different social media platforms, the data acquisition modes and interfaces may be different, and the data acquisition modes and interfaces need to be realized according to application programming interface documents of specific platforms;
the data analysis module is used for analyzing the account data of the data acquisition module, reading works, comments and transmission data of the account, and carrying out delivery analysis, content analysis and transmission analysis;
the bid analysis needs to read massive data for analysis, and adopts parallelization and distributed computing technology to divide the data and tasks into small blocks according to the displayed classification for parallel processing so as to improve the analysis speed and efficiency;
Dividing the whole analysis task into modules for independent calculation according to classification, reporting the state after the independent calculation of the modules is completed, and uniformly reporting the calculation modules of the whole task to the task management module after the completion of the calculation of the whole task;
For data storage designed to operate asynchronously, adopting a queue mode to reduce the pressure of the data storage;
The data display module is used for analyzing data display, namely converting abstract data into visual information in the form of a chart, a graph and a map;
The data after analysis is stored according to the task result, the data is stored separately according to the dimension of analysis and is bound with the unique ID of the task, the analysis result is required to support the operations of screening, sorting, filtering and the like of the data, and the design of the analysis result table is required to ensure the response speed and stability of the system.
Specifically, audience characteristics of the bid account, including age, gender, interests and the like, are analyzed, a user is helped to locate a target audience more accurately, content location and marketing strategies of the user are adjusted accordingly, appropriate content and marketing strategies are recommended to the user, preemption is helped, and a competitive advantage is maintained.
In particular, the system provides user-friendly data reporting and visualization charts that enable users to easily view and understand data analysis results, quickly make decisions and adjust strategies.
Specifically, the social media account number bid analysis system provides comprehensive and timely bid information and data support for users, helps the users to better understand the market environment, optimizes own operation strategies and improves the competitiveness.
Specifically, the bid analysis needs to read massive data for analysis, a parallelization and distributed computing technology is adopted, the data and tasks are divided into small blocks according to the displayed classification for parallel processing, so that analysis speed and analysis efficiency are improved, the whole analysis task is divided into modules for independent calculation according to the classification, the modules for independent calculation are reported after the completion of the independent calculation, and the task management modules are uniformly reported after the completion of the calculation modules of the whole task, so that high cohesion and low coupling of the system are realized, and expandability and parallel processing capacity of the system are improved.
Referring to fig. 3, a schematic structural diagram of electronic equipment components used for analyzing a bid in the present embodiment is shown.
Wherein, the processor 601 and the memory 602 complete communication with each other through the bus 603;
The processor 601 is configured to call program instructions in the memory 602 to perform the methods provided in the above method embodiments, for example, including creating and managing tasks, updating and sorting source data based on the acquisition range timing, performing bid analysis based on the source data, recording the analyzed data to the memory 602 according to a presentation report format, and performing presentation and custom export of the data based on the data of the memory 602.
Referring to fig. 4, a flowchart of a process for comparing data acquired by the bid analysis method according to the present embodiment includes,
Step S310, determining an analysis dimension, generating a data report based on the analysis dimension, and recommending an operation strategy according to the data report;
step S320, analyzing account numbers, wherein the analysis contents comprise the released positions, the released time periods, the account number types, the released quantity and the operation main body of the account numbers;
Step S330, based on the work propagation data, the highest and average data are sorted out, and the fan-image, the age distribution and the area distribution are generated according to the data of the participated users.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features can be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. The bid analysis method is characterized in that based on works, comments and sound volume data published by social media account numbers, a bid throwing account number, throwing content and spreading conditions of the bid are screened out, and product characteristics, function advantages and disadvantages of the bid and real demands and feedback of users are analyzed;
Creating an analysis task, and determining keywords of products and related bid products, wherein the keywords do not contain keywords and time ranges of the products;
collecting works, comments, sound volume, fan and interactive user data of a social media account, and storing the works, comments, sound volume, fan and interactive user data through a storage medium;
Analyzing the acquired data stored in the storage medium by using a bid analysis tool package, performing data analysis according to three dimensions of delivery analysis, content analysis and propagation analysis, and storing the analyzed data through the storage medium;
displaying analysis results according to the three dimensions of the release analysis, the content analysis and the propagation analysis, and formulating marketing and popularization strategies according to the analysis results to predict industry development trend;
Determining bid scores according to bid video praise numbers, forwarding times and comment times;
Setting a compensation parameter of the first-level flow praise number for scoring the bid, a compensation parameter of the second-level flow praise number for scoring the bid, the first-level flow equal praise number, the second-level flow equal praise number and the second-level flow equal praise number;
Determining influence supplementary parameters of the number of praise to the bid scoring according to the comparison result of the preset parameters and the actual praise, and calculating the corresponding bid scoring;
Judging whether to bring the bid into a detection range or not based on the comparison result of the bid score and a preset bid score;
Setting bid scoring as Wherein n is a bid video number, i=1, 2,..n, di is a bid i video number, K1 is a bid i video number impact compensation parameter, K2 is a bid i video number impact compensation parameter, pi is a bid i video number of comments, and K3 is a bid number impact compensation parameter for the bid score;
setting K1=K10+K11, wherein K10 is a compensation parameter related to the number of first-level flow praise and bid scores, and K11 is a compensation parameter related to the number of second-level flow praise and bid scores;
The system sets the first-level flow equal praise number Dz1, the first-level flow equal praise number Dz2, the second-level flow equal praise number Di 1, the second-level flow equal praise number Di2,
In this embodiment, if 0 is less than or equal to Di < Dz1, k10=k101, where K101 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number 0 is less than or equal to D101< Dz1, and d101=0.2 is set;
If dz1 is less than or equal to Di is less than or equal to dz2, k10=k102, wherein K102 is an influence supplementary parameter of the first-order flow praise number on the bid grade when praise number dz1 is less than or equal to Di is less than or equal to dz2, and d102=0.5 is set;
if Di > Dz2, k10=k103, where K103 is an influence supplementary parameter of the first-order flow praise number on the bid score when praise number Di > Dz2, and d103=0.8 is set;
if 0 is less than or equal to Di < Di 1, k11=k111, where K111 is an influence supplementary parameter of the secondary flow praise number on the bid score when the praise number 0 is less than or equal to Di < Di 1, and d111=0.3 is set;
If Di 1< Di2, k11=k112, where K112 is an influence supplementary parameter of the second-level flow praise number on the bid score when praise number Di 1< Di2, d112=0.6 is set;
If Di > Di2, k11=k113, where K113 is an influence supplementary parameter of the secondary flow praise number on the bid score when praise number Di > Di2, and d113=0.9 is set;
The system is provided with a first bid score F1, a second bid score F2,
If F < F1, the bid is a third bid, and the monitoring range is not included;
If F1 is not less than F2, the bid product is a second bid product and is included in the monitoring range;
if F > F2, the bid is a first bid, and the bid is included in the monitoring range and analyzed.
2. The method of claim 1, wherein a task management system is established for planning, executing and monitoring a bid analysis process, determining keywords of a bid analysis task based on a product profile and a product nickname, and generating task fields based on the keywords, wherein the task fields include a unique ID, a task state, an analysis time, keywords, and an analysis dimension.
3. The bid item analysis method according to claim 2, wherein work data, comment data, sound volume data and interaction data of an account number are acquired through a data mining technology and an open application programming interface technology and stored through a storage medium as a data source of bid item analysis;
The acquired data comprises account information, work information, comment information, sound volume information and interaction information, and corresponding fields are generated, wherein the corresponding fields comprise unique ID, acquisition content, acquisition type and acquisition time.
4. The method of claim 3, wherein the multi-dimensional analysis of the collected data based on the keywords and time frame of the task comprises,
The method comprises the steps of putting analysis, namely analyzing account advertisement putting conditions of the bid on social media, wherein an analysis result comprises an putting channel, putting time, a putting form, a putting account, a putting quantity and an operation main body of the account;
Content analysis, namely analyzing work information which accords with task keywords and comment information corresponding to the works and is published by a put account, extracting key paragraphs of the work information by using a natural language processing technology, classifying the key paragraphs and the comment information according to analysis tags, and carrying out emotion analysis on the key paragraphs and the comment information, wherein analysis results comprise the number of the works, the types and the occupation ratios of the works, analysis tags and corresponding work paragraphs, the work paragraphs and corresponding comment emotion analysis and occupation ratios;
The method comprises the steps of propagation analysis, analysis of sound volume data and interaction user information, wherein the sound volume data accords with task keyword works, the sound volume data comprises reading, commenting, sharing, forwarding, coin inserting and interaction data, the interaction user information comprises geographic positions, age groups and sexes of users, and analysis results comprise TOP values, average values, interaction user area distribution, age distribution and fan portraits of sound volumes.
5. The bid analysis method of claim 4, wherein the dimension divides the task into a plurality of subtasks, the subtask is analyzed in parallel by a distributed technology, and a subtask field is generated, wherein the subtask field comprises a subtask unique ID, an analysis task unique ID, a state and time, the state is updated after the subtask analysis is completed, and the analysis task checks the subtask analysis progress through the subtask associated unique ID.
6. The method of claim 5, wherein the analysis task is performed by storing and updating the analysis result via a storage medium to generate a task result field, the field including a task result unique ID, an analysis task unique ID, an analysis result, and a storage time.
7. A bid analysis system for implementing the bid analysis method of any one of claims 1-6, comprising,
The task management module is used for managing and monitoring the whole bid analysis flow, including task creation, distribution, state tracking, reminding and notification;
The data acquisition module acquires the work data, comment data, sound volume data and interaction data of the bid selection input account based on the social media platform, acquires related data from the target social media platform regularly or in real time by adopting a data grabbing tool or an application programming interface, stores and processes the related data, and provides data support for subsequent bid analysis;
the bid analysis module is used for carrying out analysis tasks on the collected data according to three dimensions of delivery analysis, content analysis and propagation analysis, adopting a distributed technology for parallel analysis, creating subtask information through a storage medium, maintaining the subtask state, and storing analysis results for data display after analysis is completed;
and the data display module displays the results of the bid analysis to the user, wherein the results comprise reports, charts and data visualization.
8. A storage medium having stored therein a plurality of instructions, wherein the instructions are loaded by a processor to perform the steps of the bid analysis method of any of claims 1-6.
9. An electronic device comprising said storage medium and a processor for executing instructions in said storage medium of claim 8.
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