CN110765283B - Statistical method of multimedia industry data - Google Patents

Statistical method of multimedia industry data Download PDF

Info

Publication number
CN110765283B
CN110765283B CN201911004761.3A CN201911004761A CN110765283B CN 110765283 B CN110765283 B CN 110765283B CN 201911004761 A CN201911004761 A CN 201911004761A CN 110765283 B CN110765283 B CN 110765283B
Authority
CN
China
Prior art keywords
data
index
value
multimedia
amount data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201911004761.3A
Other languages
Chinese (zh)
Other versions
CN110765283A (en
Inventor
袁婷
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Niantong Technology Co ltd
Original Assignee
Beijing Niantong Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Niantong Technology Co ltd filed Critical Beijing Niantong Technology Co ltd
Priority to CN201911004761.3A priority Critical patent/CN110765283B/en
Publication of CN110765283A publication Critical patent/CN110765283A/en
Application granted granted Critical
Publication of CN110765283B publication Critical patent/CN110765283B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/40—Information retrieval; Database structures therefor; File system structures therefor of multimedia data, e.g. slideshows comprising image and additional audio data
    • G06F16/43—Querying
    • G06F16/435—Filtering based on additional data, e.g. user or group profiles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Finance (AREA)
  • Strategic Management (AREA)
  • General Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Multimedia (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a statistical method of multimedia industry data, which comprises the following steps: s101, acquiring multimedia data and new media heat data of the same theme; s102, carrying out comprehensive weighted calculation on the multimedia data and the new media heat data to obtain an IP value index of the same theme; s103, displaying the IP value index and simultaneously displaying the multimedia data and the new media heat data in a graph form. According to the invention, on one hand, the industry practitioner can judge the value of the theme in the market through the IP value index, so that the industry practitioner can be helped to make corresponding market measures, and the value of the industry in the market is further improved; on the other hand, industry practitioners can know all industrial data of any subject of teenagers or children through chart forms, so that great information assistance is brought to practitioners, the practitioners are helped to know market quotations of all subjects in the teenagers or the children, and the practitioners can make correct market value assessment.

Description

Statistical method of multimedia industry data
Technical Field
The invention belongs to the technical field of data statistics, and particularly relates to a statistical method of multimedia industry data.
Background
For teenagers and children, there is a large market in which many practitioners such as animation manufacturers, publishers, audio and video platforms, literature platforms, toy manufacturers and the like are active, but these industries are closely related, for example, a famous animation film can be derived from related industries such as audio, books, cartoon, toys and the like.
For the market, the data of each industry has a great indication effect on the development of the industry, but some existing industry data statistics tools only collect and display the industry data on one side and cannot carry out comprehensive data statistics on the industry of the same main body. Such as: for the industry of the same subject, some statistical platforms only count the box office statistics of the animation movies, but do not count the network play amount of the movies; the playing amount statistics is not carried out on the animation drama, or the playing amount data statistics is carried out on the animation drama only, the industrial data such as books, cartoon pictures and toys derived from the animation drama are only the data in the industry, and the comprehensive statistics is not carried out on all industries of the same theme, so that industry personnel cannot recognize the comprehensive value of a teenager or children theme in the market, and the later development of the industry is affected.
Disclosure of Invention
The invention aims to provide a data statistics method capable of comprehensively counting the data of all related industries of the same theme and helping industry personnel to comprehensively know the market value of the theme, so as to solve the problem that the existing industry statistics tool can only collect and display the industry data on one side.
The technical scheme adopted by the invention is as follows:
a statistical method of multimedia industry data, comprising the steps of:
s101, acquiring multimedia data and new media heat data of the same theme;
s102, carrying out comprehensive weighted calculation on the multimedia data and the new media heat data to obtain an IP value index of the same theme;
s103, displaying the IP value index, and simultaneously displaying the multimedia data and the new media heat data in a graph form.
Preferably, the multimedia data of the same subject in the step S101 includes visual media data and auditory media data, and the new media heat data includes hundred degrees index, microblog index, network news number, weChat article number, 360 information number and hundred degrees information number.
Preferably, the visual media data comprises video play volume data, book sales volume data and toy sales volume data, and the auditory media data comprises audio play volume data, wherein the video play volume data comprises play volume data in a designated area and play volume data outside the designated area.
The step of deriving the IP value index of the same topic in the step S102 specifically includes the following steps:
s102a, obtaining a data original index of the same theme according to the multimedia data and the new media heat data;
s102b, carrying out standardized function processing on the original indexes, and obtaining the IP value indexes of the same theme after the processing is completed.
Optimally, setting the original index as lambda, the 360 information number as A, the network news number as B, the hundred degree information number as C, the hundred degree index as D, the WeChat article number as E, the microblog index as F, the play amount data in the appointed area as G, the play amount data outside the appointed area as H, the book sales amount data as I, the toy sales amount data as J and the audio play amount data as K;
before the step S102a, the difference values of the I A-B I, the I A-C I and the I B-C I are obtained, and the difference values of the three are judged;
if the value of the I A-B I, the I A-C I and the I B-C I is the smallest, the calculation formula of the original index is as follows:
if the value of the I A-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
if the value of the I B-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
preferably, the normalization function processing in the step S102b is any one of min-max normalization processing, log function conversion processing, atan function conversion processing, and Z-score normalization processing.
Preferably, the multimedia data further comprises cartoon reading quantity data and cartoon sales quantity data.
Optimally, the reading amount of the network works of the same theme can be obtained while the multimedia data and the new media heat data are obtained in the step S101.
Preferably, in step S103, the chart is in the form of one or more of a bar chart, a line chart, and a pie chart.
The beneficial effects of the invention are as follows:
(1) The invention provides a statistical method of multimedia industry data, which comprises the steps of firstly acquiring multimedia data and new media heat data of the same theme, namely, counting all the industry data of the theme, then carrying out comprehensive weighted calculation on the multimedia data and the new media heat data to obtain an IP value index of the theme, and simultaneously displaying the multimedia data and the new media heat data in a chart form.
Through the design, on one hand, an industry practitioner can judge the value of the theme in the market through the IP value index, so that the industry practitioner is facilitated to make corresponding market measures, and the value of the industry in the market is further improved; on the other hand, the industry practitioner can know all industrial data of any theme of teenagers or children through a chart form, such as video play amount data, book sales amount data, toy sales amount data and the like of the theme can be known according to the chart, so that great information assistance is brought to the practitioner, the practitioner is helped to know market quotations of various themes in the teenagers or the children, and the practitioner is helped to make correct market value assessment.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart illustrating a step of a statistical method of multimedia industry data according to the present invention.
Fig. 2 is a attention trend chart of bear showing and showing provided by the invention.
FIG. 3 is a plot of the sensitivity duty cycle of the present invention for the presence of a bear.
Fig. 4 is a bar chart of informativeness of the bear presence provided by the invention.
Fig. 5 is a graph of media liveness ratio for bear presence provided by the present invention.
Fig. 6 is a data graph of the hundreds of degrees index of bear presence provided by the present invention.
FIG. 7 is a diagram of hundred degree information data of the presence of a bear according to the present invention.
Detailed Description
The invention is further illustrated below in connection with specific examples. It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention.
Embodiments of the present invention will be understood more fully from the detailed description given below and from the accompanying drawings of various embodiments of the invention, which, however, should not be taken to limit the invention to the specific embodiments, but are for explanation and understanding only.
The term "and/or" is merely an association relationship describing an associated object, and means that three relationships may exist, for example, a and/or B may mean: the terms "/and" herein describe another associative object relationship, indicating that there may be two relationships, e.g., a/and B, may indicate that: the character "/" herein generally indicates that the associated object is an "or" relationship.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates to the contrary. It will be further understood that the terms "comprises," "comprising," "includes," "including" and/or "including," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, and do not preclude the presence or addition of one or more other features, numbers, steps, operations, elements, components, and/or groups thereof.
It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if" as used herein may be interpreted as "at … …" or "at … …" or "responsive to a determination", depending on the context.
Furthermore, the particular features, structures, functions, or characteristics may be combined in any suitable manner in one or more embodiments. For example, a first embodiment may be combined with a second embodiment as long as the particular features, structures, functions, or characteristics associated with the two embodiments are not mutually exclusive.
Example 1
As shown in fig. 1, the statistical method of the multimedia industry data provided in the present embodiment includes the following steps:
s101, acquiring multimedia data and new media heat data of the same theme.
The step S101 is to count all industries of any subject of teenagers or children, that is, all industries of the subject include multimedia data and new media caloric data.
In this embodiment, any theme of teenagers or children may be an animation movie and its derived industries, such as derived animation, toys, books, audio, etc., which all belong to the animation movie industry, and also include the search times of each browser, the number of news articles, the number of WeChat articles, etc.
Specific examples are given below, for example, a transformers movie, and industries thereof include a box office brought by a transformers movie itself, and also include a network playing amount, a transformers toy, a transformers book, a transformers audio playing amount, and the like, which are industries belonging to the theme of transformers. Meanwhile, the method also comprises the searching times of the transformers in each browser, the number of news articles appearing on the transformers, weChat articles and the like.
In this embodiment, the acquisition of multimedia data and new media heat data employs a distributed network crawling technique.
The distribution is a network crawling technology, also called a distributed web crawler technology, which is a data acquisition algorithm and is a prior art.
The distributed crawler technology can be divided into a plurality of distributed levels, and the large distributed crawler is mainly divided into 3 levels: distributed data center, distributed crawling server and distributed crawler program. The whole crawler system is composed of a plurality of global distributed data centers, each data center is responsible for capturing Internet webpages around a local area, for example, european data centers in Europe capture webpages in European countries such as England, france and Germany, and Asia data centers capture webpages in countries such as China, japan and Korea; each data center is composed of a plurality of grabbing servers connected through a high-speed network, a plurality of crawler programs can be deployed on each server, and timeliness and comprehensiveness of the data in the living area can be guaranteed through the multi-level distributed crawler system.
Therefore, the multimedia data and the new media heat data can be timely and comprehensively acquired through a distributed network crawling technology.
S102, carrying out comprehensive weighted calculation on the multimedia data and the new media heat data to obtain the IP value index of the same theme.
The step S102 performs comprehensive weighted calculation according to the acquired multimedia data and new media heat data to obtain the IP value index of the same theme.
In this embodiment, the IP (Tntellectual property, intellectual property) value is a criterion for evaluating the market value of a teenager or child theme, and having a good IP value may have the following advantages:
(1) Powerful vermicelli viscosity and vermicelli UGC (User Generated Content, internet language, i.e. user generated content) capability, deepen emotion so as to bring vermicelli scale effect; (2) The whole cognition capability of the product is strong, and various expression forms can be mutually promoted to form annular circulation; (3) Branding and monopolization make IP more susceptible to copyright protection and legal dependence; (4) The method can continuously enable the downstream rendering capability of the IP sub-to be continuous, thereby feeding back the original upstream and continuously promoting the economic chain.
Therefore, the IP value index is a measure of the IP value, and the higher the index is, the greater the IP value of a topic is, and the higher the economic benefit is.
S103, displaying the IP value index, and simultaneously displaying the multimedia data and the new media heat data in a graph form.
The step S103 can enable the practitioner to comprehensively understand the market value of a subject in the industry, i.e. judge whether the value of the subject in the market can be developed for a long time or not according to the IP value index, and whether the economic effect can be higher or not.
The multimedia data and the new media heat data are displayed in a graphic form, so that a practitioner can know the industrial distribution condition of a theme in the industry more clearly and conveniently, and the practitioner can recognize the graphic form according to the theme and recognize the market value of the theme in the industry conveniently.
In this embodiment, the transformers subject set forth above may be used to display the playing amount ratio of the movie or animation by using a bar chart or a pie chart; the sales volume of toys and books is the ratio. Therefore, the staff can more quickly know the ratio of the transformers in the market, and further the market value of the transformers is obtained.
Preferably, the multimedia data of the same subject in the step S101 includes visual media data and auditory media data, and the new media heat data includes hundred degrees index, microblog index, network news number, weChat article number, 360 information number and hundred degrees information number.
Preferably, the visual media data comprises video play volume data, book sales volume data and toy sales volume data, and the auditory media data comprises audio play volume data, wherein the video play volume data comprises play volume data in a designated area and play volume data outside the designated area.
In this embodiment, the multimedia data includes play amount data in a designated area, play amount data outside a designated area, book sales amount data, toy sales amount data, and audio play amount data. Through the design, the industry of any subject of teenagers or children can be included, and data of the subject can be acquired as much as possible.
In this embodiment, the video playing amount data further includes playing amount data in the designated area and playing amount data outside the designated area. The designated area is exemplified as China in this embodiment, and any area except China is arranged outside the designated area.
The hundred degree index is: the internet user searches the key words for attention degree and continuous change condition.
The algorithm adopted is as follows: based on the hundred-degree search amount of netizens as data, and using keywords as statistical objects, scientifically analyzing and calculating the weighting of the search frequency of each keyword in hundred-degree webpage search. The search index is divided into a PC search index and a mobile search index according to the source of the data.
In this embodiment, the hundred degree index includes the global daily average, the moving daily average, the global homonym, the global ring ratio, the moving homonym, and the moving ring ratio of the searched keywords. Wherein, the average daily value is: searching for an average value of the index days in a period of time; the same ratio is: rate of change in the same ratio as in the last year; the ring ratio is: the rate of change of the ring ratio with the last adjacent time period (the last 7 days/30 days).
The microblog index comprises the attention, sensitivity, information and media activity of a keyword, and a chart of the four data.
The network news quantity is the quantity of news articles taking the keyword or the theme as a carrier by the network.
The text number of the WeChat is the word number of the keyword or the theme appearing on the WeChat.
The 360 information number and the hundred degree information number are the search times of the keyword or the topic on the 360 browser and the hundred degree browser.
Through statistics of the multimedia data and the new media heat data, comprehensive statistics of all industrial data of any subject of teenagers or children can be completed, and a data basis is provided for subsequent calculation of IP value indexes.
The step of deriving the IP value index of the same topic in the step S102 specifically includes the following steps:
s102a, obtaining the data original index of the same theme according to the multimedia data and the new media heat data.
After the multimedia data and the new media heat data of the same theme are obtained, calculation can be performed, so that the original data index of the same theme is obtained, and further, data support is provided for standardized function processing.
S102b, carrying out standardized function processing on the original indexes, and obtaining the IP value indexes of the same theme after the processing is completed.
And (3) carrying out standardized function processing on the data original indexes to obtain the IP value indexes of the same theme.
Optimally, setting the original index as lambda, the 360 information number as A, the network news number as B, the hundred degree information number as C, the hundred degree index as D, the WeChat article number as E, the microblog index as F, the play amount data in the appointed area as G, the play amount data outside the appointed area as H, the book sales amount data as I, the toy sales amount data as J and the audio play amount data as K;
before the step S102a, the difference values of the I A-B I, the I A-C I and the I B-C I are obtained, and the difference values of the three are judged;
if the value of the I A-B I, the I A-C I and the I B-C I is the smallest, the calculation formula of the original index is as follows:
if the value of the I A-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
if the value of the I B-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
preferably, the normalization function processing in the step S102b is any one of min-max normalization processing, log function conversion processing, atan function conversion processing, and Z-score normalization processing.
The data original index of the same theme can be calculated through the three different formulas, and finally, the IP value index of the same theme can be obtained after the data original index of the same theme is processed through a standardized function.
The standardized function processing is an existing algorithm, and is a method in statistics, and mainly comprises Min-max standardization (Min-max normalization), log function conversion, atan function conversion and z-score standardization.
Because the evaluation indexes are different in property and generally have different dimension and magnitude, when the level of each index is greatly different, if the data original index is used for analysis, the effect of the index with higher value in comprehensive analysis is highlighted, and the effect of the index with lower value level is relatively weakened, so that in order to ensure the reliability of the result, the data original index is required to be standardized so as to ensure the reliability of the IP value index, and the value of the industry in the market can be reflected more truly.
Preferably, the multimedia data further comprises cartoon reading quantity data and cartoon sales quantity data.
Through the design, the comprehensiveness of the industrial data of any theme of teenagers or children can be further increased, the industrial data is displayed through a graphic table, and the industrial personnel can conveniently and comprehensively master the value of the theme of the teenagers or children in the market by combining other data in the multimedia data and new media heat data.
In this embodiment, not only cartoon data under the same subject of teenagers or children can be collected. Original cartoon data can also be collected, because some subjects are only in the form of a cartoon, so in order to ensure the comprehensiveness of the data, it is necessary to collect the original cartoon, i.e. the data of the subject only in the form of a cartoon.
The original cartoon data can be obtained from a domestic main stream cartoon platform through a distributed network crawling technology, wherein the platform can be used for but not limited to: tencerting cartoon, ming dynasty Tai Ji, film cartoon, station b, cartoon, monster and net cartoon. People can acquire the popularity data, the collection data, the comment data and the praise data of a piece of original cartoon from the platforms, and the popularity of the original cartoon is judged according to the data.
Similarly, various data of the original cartoon can be displayed in various chart forms, so that a practitioner can more intuitively see various data of a certain original cartoon, and further, the popularity of the original cartoon is judged.
Optimally, the reading amount of the network works of the same theme can be obtained while the multimedia data and the new media heat data are obtained in the step S101.
By acquiring the reading quantity of the online literacy works of the same theme, the industrial data of teenagers or children under the same theme can be counted more comprehensively, so that the industrial data under the same theme is more comprehensive. Meanwhile, the reading quantity of the counted net work can be displayed in a chart form, and visual analysis of practitioners is facilitated.
In this embodiment, the acquisition of the internet text data is also realized by a distributed network crawling technology, which can be, but is not limited to, acquisition from the following platforms: start point, QQ reading, jin river, horizontal and vertical Chinese net, 17K and palm reading. Meanwhile, in order to further perfect the comprehensiveness of the network works, besides the reading quantity of the network works, month ticket information data, comment information data, collection information data and ranking information data on each website of the network works can be acquired, and through the data, a practitioner can be helped to judge the popularity of a certain network works and judge the market value of the network works more accurately.
The scheme is optimized as follows: because the good internet works can be changed into cartoon and audio readings, in order to further perfect the comprehensiveness of various industrial data of the same theme, the data statistics can be carried out on the cartoon and the audio readings which are changed by the internet works, namely, the reading quantity of the cartoon and the playing quantity of the audio readings are counted, and the cartoon and the audio readings can be displayed in a chart form, so that practitioners can be helped to intuitively know all the industries of the theme in the market, further comprehensively grasp the occupation ratio of the theme in various industries of the market, and more comprehensively reflect the market value of the theme.
Preferably, in step S103, the chart is in the form of one or more of a bar chart, a line chart, and a pie chart. Through the design, more types of graphic tables are used, so that industry personnel can more intuitively see data of different industries under one theme, and market value can be obtained more conveniently.
In the embodiment, the animation playing amount data and the animation heat data of teenagers and children in different foreign countries can be obtained, and data support can be made for foreign animation introduction into domestic markets according to the two data.
Example two
As shown in fig. 2 to 6, the following is an example of the statistical method of the multimedia industry data described in the first embodiment:
taking the current bear showing and missing animation as an example, the IP value index calculation process of the bear showing and missing is elaborated:
firstly, acquiring domestic video play amount data, foreign video play amount data, book sales amount data and toy sales amount data of bear through a distributed network crawling technology;
simultaneously acquiring hundreds of degree indexes, microblog indexes, network news data, the number of WeChat articles, 360 information numbers and hundreds of degree information description of the bear;
the data in the microblog indexes of the bear are shown in fig. 2 to 5: fig. 2 shows a attention trend graph of bear, fig. 3 shows a sensitivity ratio graph, fig. 4 shows an informativeness bar graph, and fig. 5 shows a media activity ratio graph of bear. From these figures, various data in the microblog index can be derived.
Fig. 6 is a graph of hundred degree index data for bear and is provided with global daily average, moving daily average, global homonymy, global ring ratio, moving homonymy and moving ring ratio data.
FIG. 7 is a graph showing the hundred degree information data of the bear. The hundred degree index and the hundred degree information number of the bear can be obtained through the figures 6 and 7. Similarly, other data in the new media heat data may also be derived from distributed network crawling techniques.
Judging the magnitude of the difference value between 360 information numbers of bear, the network news number and hundred degree information numbers, and selecting different calculation formulas of the initial index in the first embodiment according to the magnitude of the difference value in the three information numbers to calculate;
and finally, carrying out standardized function processing on the calculated original index, and obtaining the IP value index of the bear after the processing is finished.
The invention is not limited to the above-described alternative embodiments, and any person who may derive other various forms of products in the light of the present invention, however, any changes in shape or structure thereof, all falling within the technical solutions defined in the scope of the claims of the present invention, fall within the scope of protection of the present invention.

Claims (5)

1. A method for counting multimedia industry data, comprising the steps of:
s101, acquiring multimedia data and new media heat data of the same theme;
s102, carrying out comprehensive weighted calculation on the multimedia data and the new media heat data to obtain an IP value index of the same theme;
s103, displaying the IP value index, and simultaneously displaying the multimedia data and the new media heat data in a chart form;
the multimedia data of the same subject in the step S101 includes visual media data and auditory media data, and the new media heat data includes a hundred degree index, a microblog index, a network news number, a WeChat article number, a 360 information number and a hundred degree information number;
the visual media data comprises video play amount data, book sales amount data and toy sales amount data, and the auditory media data comprises audio play amount data, wherein the video play amount data comprises play amount data in a designated area and play amount data outside the designated area;
the step of deriving the IP value index of the same topic in step S102 specifically includes the following steps:
s102a, obtaining a data original index of the same theme according to the multimedia data and the new media heat data;
s102b, carrying out standardized function processing on the original indexes, and obtaining the IP value indexes of the same theme after the processing is completed;
setting the original index as lambda, the 360 information number as A, the network news number as B, the hundred degree information number as C, the hundred degree index as D, the WeChat article number as E, the microblog index as F, the play amount data in the appointed area as G, the play amount data outside the appointed area as H, the book sales amount data as I, the toy sales amount data as J and the audio play amount data as K;
before the step S102a, the difference values of the I A-B I, the I A-C and the B-C I are obtained, and the difference values of the I A-B I, the I A-C and the B-C I are judged;
if the value of the I A-B I, the I A-C I and the I B-C I is the smallest, the calculation formula of the original index is as follows:
if the value of the I A-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
if the value of the I B-C is the smallest in the I A-B I, the I A-C I and the I B-C I, the calculation formula of the original index is as follows:
2. the method of claim 1, wherein the step of: the normalization function processing in the step S102b is any one of min-max normalization processing, log function conversion processing, atan function conversion processing and Z-score normalization processing.
3. The method of claim 1, wherein the step of: the multimedia data further includes cartoon reading amount data and cartoon sales amount data.
4. The method of claim 1, wherein the step of: the reading amount of the internet works of the same theme can be obtained while the multimedia data and the new media heat data are obtained in the step S101.
5. The method of claim 1, wherein the step of: in the step S103, the chart is in the form of one or more of a bar chart, a line chart, and a pie chart.
CN201911004761.3A 2019-10-22 2019-10-22 Statistical method of multimedia industry data Active CN110765283B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201911004761.3A CN110765283B (en) 2019-10-22 2019-10-22 Statistical method of multimedia industry data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201911004761.3A CN110765283B (en) 2019-10-22 2019-10-22 Statistical method of multimedia industry data

Publications (2)

Publication Number Publication Date
CN110765283A CN110765283A (en) 2020-02-07
CN110765283B true CN110765283B (en) 2023-08-25

Family

ID=69332835

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201911004761.3A Active CN110765283B (en) 2019-10-22 2019-10-22 Statistical method of multimedia industry data

Country Status (1)

Country Link
CN (1) CN110765283B (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104202623A (en) * 2014-08-04 2014-12-10 杜泽壮 All media transmission index statistical method and device
CN106909618A (en) * 2017-01-13 2017-06-30 广州薏生网络科技有限公司 A kind of article of healthy class from media propagates the computational methods of combined influence power
CN107705005A (en) * 2017-09-27 2018-02-16 吴殿义 A kind of movie and television contents Valuation Method
CN109275031A (en) * 2018-09-25 2019-01-25 有米科技股份有限公司 A kind of temperature appraisal procedure, device and the electronic equipment of video
CN109816431A (en) * 2018-12-26 2019-05-28 北京中科闻歌科技股份有限公司 A kind of branding communication index construction method, system and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104202623A (en) * 2014-08-04 2014-12-10 杜泽壮 All media transmission index statistical method and device
CN106909618A (en) * 2017-01-13 2017-06-30 广州薏生网络科技有限公司 A kind of article of healthy class from media propagates the computational methods of combined influence power
CN107705005A (en) * 2017-09-27 2018-02-16 吴殿义 A kind of movie and television contents Valuation Method
CN109275031A (en) * 2018-09-25 2019-01-25 有米科技股份有限公司 A kind of temperature appraisal procedure, device and the electronic equipment of video
CN109816431A (en) * 2018-12-26 2019-05-28 北京中科闻歌科技股份有限公司 A kind of branding communication index construction method, system and storage medium

Also Published As

Publication number Publication date
CN110765283A (en) 2020-02-07

Similar Documents

Publication Publication Date Title
Sabat et al. Hate speech in pixels: Detection of offensive memes towards automatic moderation
Biber et al. Exploring the composition of the searchable web: A corpus-based taxonomy of web registers
CN103853824B (en) In-text advertisement releasing method and system based on deep semantic mining
Sharma et al. Nlp and machine learning techniques for detecting insulting comments on social networking platforms
CN105512687A (en) Emotion classification model training and textual emotion polarity analysis method and system
CN106354845A (en) Microblog rumor recognizing method and system based on propagation structures
KR20120108095A (en) System for analyzing social data collected by communication network
CN103577556A (en) Device and method for obtaining association degree of question and answer pair
Yarmand et al. " Can you believe [1: 21]?!" Content and Time-Based Reference Patterns in Video Comments
Bykau et al. Fine-grained controversy detection in Wikipedia
CN111026965A (en) Hot topic tracing method and device based on knowledge graph
Shen et al. Information retrieval of a disaster event from cross-platform social media
CN103810170B (en) Intercommunion platform file classification method and device
Leopairote et al. Software quality in use characteristic mining from customer reviews
CN105468780B (en) The normalization method and device of ProductName entity in a kind of microblogging text
Hasanati et al. Implementation of support vector machine with lexicon based for sentimenT ANALYSIS ON TWITter
Kim et al. Competitive intelligence in Korean Ramen market using text mining and sentiment analysis
CN110765283B (en) Statistical method of multimedia industry data
JP2006004098A (en) Evaluation information generation apparatus, evaluation information generation method and program
Badam et al. Aletheia: A fake news detection system for Hindi
Sun et al. EduVis: Visualization for education knowledge graph based on web data
JP2010272075A (en) Emotional information extraction device, emotion retrieval device, method thereof, and program
CN106372147B (en) Heterogeneous topic network construction and visualization method based on text network
CN111078888B (en) Method for automatically classifying comment data of social network users
HongDa et al. A recommendation method for social media users based on a sentiment analysis model

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant