CN113722440A - Significance analysis method based on keyword recognition and related product - Google Patents

Significance analysis method based on keyword recognition and related product Download PDF

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CN113722440A
CN113722440A CN202111019370.6A CN202111019370A CN113722440A CN 113722440 A CN113722440 A CN 113722440A CN 202111019370 A CN202111019370 A CN 202111019370A CN 113722440 A CN113722440 A CN 113722440A
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CN113722440B (en
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郁文剑
潘文磊
李泽华
田野
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Ping An Technology Shenzhen Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F16/3344—Query execution using natural language analysis
    • G—PHYSICS
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    • 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
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • 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
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking

Abstract

The application relates to keyword recognition in the field of artificial intelligence, and particularly provides a significance analysis method based on keyword recognition and a related product. The method comprises the following steps: acquiring a plurality of public opinion data related to a product to be analyzed; identifying the public opinion data to obtain at least one public opinion mark; determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed; and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance. The method and the device are beneficial to improving the accuracy of the service trend prediction.

Description

Significance analysis method based on keyword recognition and related product
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a significance analysis method based on keyword recognition and a related product.
Background
Public sentiment is generally of great significance to the development of macro-economy, industry or companies, and particularly for company marketing activities (e.g., insurance sales business, etc.), public sentiment can influence business trends and development directions.
Therefore, in order to predict the trend and development direction of the business and make a response in advance, people in various circles are interested to extract effective information from public sentiment so as to accurately predict the trend and development direction of the business. However, since the public sentiment data has many subjects, many subject categories, and many noises, effective information cannot be extracted from the public sentiment, so that the predicted direction and tendency have low accuracy.
How to effectively extract information related to product business development from public sentiment is a problem to be solved urgently at present.
Disclosure of Invention
The embodiment of the application provides a significance analysis method based on keyword recognition and related products, and public sentiment marks related to product service development can be extracted from public sentiment data by establishing the public sentiment marks and calculating quantitative indexes of the public sentiment marks, so that the direction and trend of the product service can be accurately predicted.
In a first aspect, an embodiment of the present application provides a method for analyzing significance based on keyword recognition, including:
acquiring a plurality of public opinion data related to a product to be analyzed;
identifying the public opinion data to obtain at least one public opinion mark, wherein the at least one public opinion mark is determined according to the entity and the theme of the public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
In a second aspect, embodiments of the present application provide a significance analysis product, including:
an acquisition unit for acquiring a plurality of pieces of public opinion data related to a product to be analyzed;
the processing unit is used for identifying the public opinion data to obtain at least one public opinion mark, and the at least one public opinion mark is determined according to the entity and the theme of the public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor coupled to a memory, the memory configured to store a computer program, the processor configured to execute the computer program stored in the memory to cause the electronic device to perform the method of the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, where the computer program makes a computer execute the method according to the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, the computer being operable to cause a computer to perform the method according to the first aspect.
The embodiment of the application has the following beneficial effects:
it can be seen that, in the embodiment of the present application, a plurality of pieces of public opinion data related to a product to be analyzed are obtained, then a public opinion corresponding to the product to be analyzed is identified from the plurality of pieces of public opinion data, and each public opinion is quantized to obtain a quantization index of each public opinion, where the quantization index is used for representing the degree of influence of a target service of the product to be analyzed of each public opinion; and finally, the target public sentiment target is screened from at least one public sentiment target based on the quantitative index of each public sentiment target, so that the target public sentiment target which has significant influence on the service development is extracted from the public sentiment data, namely, the information related to the service development of the product is effectively extracted, the blank of the prior art is made up, and the precision of the trend of the prediction service based on the target public sentiment target is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for analyzing a significance based on keyword recognition according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a significance analysis provided by an embodiment of the present application;
FIG. 3 is a schematic illustration of another significance analysis provided by an embodiment of the present application;
fig. 4 is a block diagram illustrating functional units of a significance analysis apparatus according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology, for example, entity and theme identification is performed on public opinion data based on a keyword identification technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Referring to fig. 1, fig. 1 is a schematic flowchart of a saliency analysis method based on keyword recognition according to an embodiment of the present application. The method employs a significance analysis apparatus. The method comprises the following steps:
101: the significance analysis device acquires a plurality of public opinion data related to a product to be analyzed.
Illustratively, the product to be analyzed may be any physical product, such as a vehicle, a cosmetic, a beverage, etc.; or any virtual product such as insurance, funds, etc. In the present application, the product to be analyzed is taken as an example of adult insurance, and the type of the product to be analyzed is not limited.
Illustratively, the significance analysis device acquires a plurality of pieces of original public opinion data from a plurality of third-party platforms (for example, news media platforms) through a crawler technology, and then performs keyword recognition screening and filtering on the plurality of pieces of original public opinion data respectively to obtain a plurality of pieces of public opinion data related to a product to be analyzed.
102: the significance analysis device identifies a plurality of pieces of public opinion data to obtain at least one public opinion mark.
Wherein, at least one public opinion mark is determined according to the entity and the theme in the public opinion data. Illustratively, entity and topic recognition is performed on each piece of public opinion data through an artificial intelligence technology to obtain at least one public opinion target, for example, keyword recognition is performed on each piece of public opinion data to obtain an entity and a topic in each piece of public opinion data, and then the entity and/or the topic in each piece of public opinion data are used as the public opinion targets to obtain the at least one public opinion target, for example, the public opinion targets are punished events, rewarded events, and the like for the product to be analyzed.
Optionally, the plurality of pieces of news data include a plurality of pieces of news data and a plurality of pieces of social media data; therefore, the at least one public sentiment target may be identified from a plurality of news data, may also be identified from a plurality of social media data, and may also be extracted from the news data and the social media data according to a certain business scene logic and a certain weight distribution.
103: the significance analysis device determines a quantization index of each public opinion target according to a plurality of pieces of public opinion data, wherein the quantization index of each public opinion target is used for representing the influence degree of the target business of a product to be analyzed of the public opinion target, and the target business is any business under the product to be analyzed.
It should be noted that the quantization index of each public sentiment target is a quantization representation of each public sentiment target, for example, if the public sentiment target is a punished event, the quantization index of the quantization index is the influence degree of the punished event. Illustratively, the target service is any service under the product to be analyzed, for example, if the product to be analyzed is an adult insurance, the target service may be a refund rate, a purchase amount, and the like of the adult insurance; the application will be described with the example of a guaranteed rate for which the target service is an adult insurance.
Illustratively, for any one public opinion target, based on a plurality of pieces of news data, determining a first contribution degree of the plurality of pieces of news data to each public opinion target; determining a second contribution degree of the plurality of pieces of social media data to each public opinion target based on the plurality of pieces of social media data; and finally, determining the quantization index of each public opinion target based on the first contribution degree and the second contribution degree of each public opinion target.
The following describes a process of calculating a quantization index of a public sentiment target by taking an example of e of any public sentiment target, and the calculation manner of the quantization indexes of other public sentiment targets is similar to that of e of the public sentiment target, and will not be described again.
For example, a type and an emotion value of each piece of news data in the plurality of pieces of news data are determined, wherein the type of each piece of news data includes positive news data or negative news data, and the emotion value of each piece of news data is used for representing the degree of influence of each piece of news data on the target service of the product to be analyzed, that is, the emotion value is a quantitative value of the degree of influence. For example, if the news data is negative news data, it is known that the news data has a negative influence on the target service, but the degree of the negative influence can be reflected by the emotion value. Specifically, event extraction may be performed on each piece of news data, a news event in each piece of news data is determined, and then an emotion value of each piece of news data is determined based on a mapping relationship between the news event and the emotion value, which is established in advance.
Further, determining a first contribution degree of the plurality of pieces of news data to the public opinion target e according to the type and the emotion value of the target news data, wherein the target news data is news data of the plurality of pieces of news data which contain the public opinion target e. It should be noted that the plurality of news data may correspond to different subjects and entities, and therefore not all of the plurality of news data include the public opinion target e, and therefore, the target news data including the public opinion target e is selected from the plurality of news data, wherein the number of the target news data is one or more.
For example, the first contribution of the public sentiment e can be expressed by formula (1):
Figure BDA0003239107840000051
wherein ,
Figure BDA0003239107840000052
a first contribution degree of a plurality of news data to the public opinion mark e,
Figure BDA0003239107840000053
the number of the targeted news data belonging to the positive news data,
Figure BDA0003239107840000054
for the mood value of positive news data in the target news data, i.e.
Figure BDA0003239107840000055
It is indicated that the emotion values of the target news data belonging to the positive news data among the target news data are summed,
Figure BDA0003239107840000061
for the amount of data belonging to negative news in the target news,
Figure BDA0003239107840000062
emotional value of negative news data in target news data, i.e.
Figure BDA0003239107840000063
Representing a sum of sentiment values of target news data belonging to negative news data among the target news data, Cs，0For the total number of pieces of news data, γ1 and γ2Is a preset weight value.
Similar to the way of calculating the first contribution degree, determining the type and the emotion value of each piece of social media data in the plurality of pieces of news data, wherein the type of each piece of social media data comprises positive social media data or negative social media data, and the emotion value of each piece of social media data is used for representing the influence degree of each piece of social media data on the target business of the product to be analyzed; and determining a second contribution degree of the plurality of pieces of social media data to each public opinion according to the type and the emotion value of the target social media data, wherein the target social media data is the social media data of e containing the public opinion in the plurality of pieces of social media data.
For example, the second contribution of the public sentiment e can be expressed by formula (2):
Figure BDA0003239107840000064
wherein ,
Figure BDA0003239107840000065
a second contribution to the public opinion analysis target e for social media data,
Figure BDA0003239107840000066
representing the amount of data belonging to the front social media data in the target social media data,
Figure BDA0003239107840000067
emotional value for positive social media data, i.e.
Figure BDA0003239107840000068
For characterizing a sum of emotion values for positive social media data in the target social media data;
Figure BDA0003239107840000069
for the number of negative social media data in the target social media data,
Figure BDA00032391078400000610
in order to have a negative emotional value of the social media data,
Figure BDA00032391078400000611
means for summing sentiment values characterizing negative social media data in the target social media data; cs，0Is the total number of pieces of social media data, gamma1 and γ2Is a preset weight value.
Further, a quantitative index of the public sentiment target e is determined according to the first contribution degree and the second contribution degree of the public sentiment target e. Considering the influence of policy, financial market and event, the quantitative index of the public sentiment e can be represented by formula (3):
Figure BDA00032391078400000612
wherein ,I(e)Quantitative index for the public sentiment e, U is a market correction index including but not limited to financial or market data, P is a regulatory policy correction index, σ (t) is the impact of an incident, α0To preset the hyper-parameter, ws、wn、wu and wpIs a preset weight coefficient.
Wherein, the market modification index is determined according to macroscopic economy, industry economy, stock market, debt market or other financial state data; regulatory policy revision index: the index represents the influence index of the national or provincial supervision policy on a specific industry, and the index can comprehensively reflect the magnitude, direction, field, period length and the like of the influence of a certain policy; the impact of an emergency: similar to the regulatory policy revision index, the impact of an emergency characterizes the impact index of a specific event on a specific industry, and the impact index can comprehensively reflect the magnitude, direction, field, period, and the like of the impact of the event. The market modification index may be obtained from a financial market; the supervision policy corrects the index and the emergency influence index, and is constructed according to the development characteristics of various industries. The specific acquisition process of each index is not described.
104: the significance analysis device determines a target public sentiment target in at least one public sentiment target according to the quantization index of each public sentiment target, and the target public sentiment target has significance on the target service.
For example, the public opinion data may be the public opinion data at any time. Therefore, the quantitative index of each public opinion mark at each moment can be determined based on a plurality of public opinion data at each moment; determining a plurality of first variable quantities of a quantization index of each public opinion target in a plurality of preset time periods according to the preset time periods; similarly, a plurality of second variable quantities of the target service of the product to be analyzed in a plurality of preset time periods and a third variable quantity of the target service of the first analysis product in a plurality of preset time periods are obtained, wherein the product attribute of the first analysis product is mutually exclusive with the product attribute of the product to be analyzed, for example, if the product to be analyzed is adult insurance, the first analysis product is non-adult insurance, that is, mutually exclusive product attributes are adult and non-adult; and finally, determining the target public opinion in at least one public opinion target according to a plurality of first variable quantities of the quantization index of each public opinion target in a plurality of preset time periods, a plurality of second variable quantities of the target service of the product to be analyzed in a plurality of preset time periods and a plurality of third variable quantities of the target service of the first analysis product in a plurality of preset time periods.
Similarly, the following describes a process of determining whether the public sentiment target e is the target public sentiment target by taking the public sentiment target e, the target service as the refund rate and a plurality of times as a plurality of time of the end of month as examples, and other public sentiment targets are similar to the public sentiment target e and will not be described again.
Firstly, acquiring a plurality of pieces of public opinion data at the end of each month, and determining a quantitative index of a public opinion target e at the end of each month based on the plurality of pieces of public opinion data; if the time interval is three months, the variation of the quantization index of each public opinion target in a plurality of preset time periods can be respectively obtained every three months from the end of the month of the first month to obtain a plurality of first variations; similarly, the variation of the retirement rate of each adult insurance (such as car insurance, life insurance, serious insurance, and the like) is obtained every three months from the end of the first month, and a plurality of second variations of a plurality of non-adult insurance can be obtained in each preset time period; and acquiring the variation of the withdrawal rate of each non-adult insurance (such as baby insurance, and the like) at intervals of three months from the end of the month of the first month, wherein a plurality of third variations of the plurality of non-adult insurances can be obtained in each preset time period.
In an implementation manner of the present application, it is assumed that an influence of a change in a quantization index of a public opinion target e on a target service of a product to be analyzed is positively correlated, a first preset time period and a second preset time period in a plurality of preset time periods may be determined, where a first variation of the quantization index of the public opinion target e in the first preset time period is greater than a first threshold, where the first threshold is greater than zero, that is, the preset time period in which the quantization index of the public opinion target e changes greatly is found from the plurality of preset time periods; the absolute value of the first variation of the quantization index of the public sentiment target e in the second preset time period is smaller than the first threshold, that is, the preset time period in which the quantization index of the public sentiment target e is found from the plurality of preset time periods and has almost no variation is obtained. For example, if a first variation of the quantitative index of the public sentiment target e between the first month end and the fourth month end (i.e. within the first preset time period) is greater than a first threshold, the preset time period between the first month end and the fourth month end is taken as the first preset time period; and if the absolute value of a first variation of the quantitative index of the public opinion target e between the first month end time and the fourth month end time is smaller than a first threshold, taking a preset time period between the first month end time and the fourth month end time as a second preset time period.
Further, when a first variation of a quantitative index of a public sentiment target e in a first preset time period is greater than a first threshold, determining a first number of products to be analyzed, of which a second variation is greater than a second threshold in the first preset time period, according to a plurality of second variations of a target service of each product to be analyzed in a plurality of preset time periods, where the second threshold is greater than zero; and determining a second number of the first analysis products of which the third variation is larger than a second threshold value in the first preset time period according to the third variation of the target service of the first analysis product in a plurality of preset time periods. In brief, when the public sentiment target e is increased more within a first preset time period, then determining a first number of adult insurance with the withdrawal rate greater than a first threshold value within the first preset time period from the plurality of adult insurance, namely determining the first number of adult insurance with the withdrawal rate increased more within the first preset time period; a second amount of non-adult insurance having a de-rate greater than the first threshold for the first time period is determined from the plurality of non-adult insurance, i.e., a second amount of non-adult insurance having a de-rate that increases more over the first predetermined time period is determined.
Further, when the absolute value of a first variation of the quantitative index of the public sentiment target e in a first preset time period is smaller than a first threshold (i.e. the quantitative index is not substantially changed in the first preset time period), determining a third number of products to be analyzed, of which the absolute value of a second variation of the target service in a second preset time period is larger than the second threshold, according to second variations of the target service of each product to be analyzed in a plurality of preset time periods; determining a fourth number of the first analysis products of which the absolute values of the third variation of the target service in a second preset time period are greater than a second threshold according to the third variation of the target service of the first analysis product in a plurality of preset time periods; briefly, when the public sentiment target e does not change substantially within a second preset time period, then within the second preset time period, a third amount of adult insurance with the absolute value of the retirement rate greater than a second threshold value is determined from the plurality of types of adult insurance, namely, the third amount of adult insurance with the larger change of the retirement rate within the second preset time period is determined, and a fourth amount of non-adult insurance with the absolute value of the retirement rate greater than the second threshold value is determined from the plurality of types of non-adult insurance, namely, the fourth amount of non-adult insurance with the larger change of the retirement rate within the second preset time period is determined.
Suppose C for the product to be analyzed1Indicating, for the first analytical product
Figure BDA0003239107840000095
Showing, as shown in FIG. 2, the quantization index I when e of the public sentiment mark is within a first preset time period(e)Of (a) is Δ I(e)＞i0Then, the first number n can be obtained1And a second number m1I.e. with n1The rate of the adult insurance refund in the first preset time period is larger than a second threshold b0And has m1The rate of refuge of the adult insurance is greater than a second threshold b0, wherein ,i0Is a first threshold value; similarly, when the public sentiment target e is the quantization index I in the second preset time period(e)The absolute value of the amount of change of (1), i.e. | Δ I(e)|＜i0Then a third number n 'can be obtained'1And a fourth number m'1I.e. of n'1The rate of the adult insurance refund in the second preset time period is larger than a second threshold b0Is of m'1The rate of refunding of the adult insurance is greater than a second threshold b within a second preset time period0。
Further, a target quantization index of the public sentiment e is determined based on the first number, the second number, the third number and the fourth number.
For example, the target quantization index of the public sentiment target e can be expressed by formula (4):
E1＝E1_p+E1_qformula (4);
wherein ,E1Is used as a target quantization index,
Figure BDA0003239107840000091
Figure BDA0003239107840000092
or ,
Figure BDA0003239107840000093
Figure BDA0003239107840000094
finally, if the target quantization index is within the preset interval (sigma 1, sigma 2), determining the public sentiment target e as the target public sentiment target. Moreover, the influence of the public sentiment target e on the target business of the product to be analyzed has positive correlation, that is, when the quantitative index of the public sentiment target e is increased, the target business of the product to be analyzed is also increased.
In another embodiment of the present application, assuming that the influence of the change of the quantization index of the public opinion target e on the target service of the product to be analyzed is negative correlation, a third preset time period and a second preset time period in the plurality of preset times are determined, wherein a first variation of the quantization index of each public opinion target in the third preset time period is smaller than the opposite number of a first threshold, the first threshold is larger than zero, that is, a preset time period with a smaller reduction range of the quantization index of the public opinion target e is found from the plurality of preset time periods, and the second preset time period is a preset time period with an absolute value of the first variation of the quantization index of the public opinion target e smaller than the first threshold, that is, the quantization index of the public opinion target e is found from the plurality of preset time periods is almost unchanged;
then, similar to the manner of determining the target quantization index, according to second variation of the target service of the product to be analyzed in a plurality of preset time periods, determining a first number of the product to be analyzed, in which the second variation is smaller than the inverse number of a second threshold in a third preset time period, and the second threshold is larger than zero, that is, determining a first number of low-amplitude high-adult insurance with a reduced guarantee withdrawal rate among a plurality of kinds of adult insurance; according to third variable quantities of the target service of the first analysis product in a plurality of preset time periods, determining a second quantity of the first analysis product, of which the third variable quantities are smaller than the opposite number of a second threshold value in the third preset time period, namely determining a second quantity of the non-adult insurance with high reduction range of the retirement rate of the plurality of non-adult insurance; determining a third quantity of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than a second threshold value, according to the second variation of the target service of the products to be analyzed in a plurality of preset time periods, namely determining the third quantity of the adult insurance with the greater reduction amplitude of the rate of refuge in the second preset time period; and determining a fourth quantity of the first analysis products with the absolute value of the third variation of the target service in the second preset time period larger than a second threshold value according to the third variation of the target service of the first analysis products in a plurality of preset time periods, namely determining the fourth quantity of the non-adult insurance with the greater reduction amplitude of the retirement rate in the second preset time period.
Similarly, assume that the product to be analyzed is C1Indicating, for the first analytical product
Figure BDA0003239107840000101
Showing, as shown in FIG. 3, the quantization index I when e of the public sentiment mark is in the third predetermined time period(e)Of (a) is Δ I(e)＜-i0Then, the first number n can be obtained1And a second number m1I.e. with n1The reduction amplitude of the retirement rate of the adult insurance in the first preset time period is smaller than the opposite number of the second threshold value, namely smaller than-b0And has m1The reduction range of the insurance withdrawal rate of the young adults is less than-b0, wherein ,i0Is a first threshold value; in the same way as above, the first and second,when the public opinion mark e is in the quantization index I in the second preset time period(e)The absolute value of the amount of change of (1), i.e. | Δ I(e)|＜i0Then a third number n 'can be obtained'1And a fourth number m'1I.e. of n'1The rate of the adult insurance refund in the second preset time period is larger than a second threshold b0Is of m'1The rate of refunding of the adult insurance is greater than a second threshold b within a second preset time period0。
Finally, determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number. The manner of determining the target quantization index of the public sentiment target e in the negative correlation is similar to the above formula (4), and will not be described.
Similarly, if the target quantization index of the public sentiment target e under the negative correlation condition is within the preset interval, determining the public sentiment target e as the target public sentiment target. In addition, the influence of the quantitative index of the public sentiment target e on the target service is in negative correlation.
In one embodiment of the application, after the target public opinion target is determined, a quantitative index of the target public opinion target at the current time is obtained, and a measure related to the target service is made based on a variation amount of the target public opinion target relative to the previous time. For example, when the target public sentiment target is positively correlated with the target service, if the variation is increased by 10%, it can be predicted that the future rate of the refund will also increase, so that measures related to the refund are made in advance, thereby providing a direction for planning the target service.
It can be seen that, in the embodiment of the present application, a plurality of pieces of public opinion data related to a product to be analyzed are obtained, then a public opinion corresponding to the product to be analyzed is identified from the plurality of pieces of public opinion data, and each public opinion is quantized to obtain a quantization index of each public opinion, where the quantization index is used for representing the degree of influence of a target service of the product to be analyzed of each public opinion; and finally, the target public sentiment target is screened from at least one public sentiment target based on the quantitative index of each public sentiment target, so that the target public sentiment target which has significant influence on the service development is extracted from the public sentiment data, namely, the information related to the service development of the product is effectively extracted, the blank of the prior art is made up, and the precision of the trend of the prediction service based on the target public sentiment target is improved.
Referring to fig. 4, fig. 4 is a block diagram illustrating functional units of an xx device according to an embodiment of the present disclosure. The electronic device 400 includes: an acquisition unit 401 and a processing unit 402;
an obtaining unit 401, configured to obtain a plurality of pieces of public opinion data related to a product to be analyzed;
a processing unit 402, configured to identify the pieces of public opinion data to obtain at least one public opinion target, where the at least one public opinion target is determined according to an entity and a theme of the pieces of public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
In some possible embodiments of the present application, the public opinion data includes news data and social media data, and in terms of determining a quantization index of each public opinion target according to the public opinion data, the processing unit 402 is specifically configured to:
determining a first contribution degree of the plurality of pieces of news data to each public opinion mark;
determining a second degree of contribution of the plurality of pieces of social media data to each of the public sentiments;
and determining the quantization index of each public opinion target according to the first contribution degree and the second contribution degree of each public opinion target.
In some possible embodiments of the present application, in determining the first contribution degree of the plurality of pieces of news data to each of the public opinion marks, the processing unit 402 is specifically configured to:
determining the type and emotion value of each piece of news data in the plurality of pieces of news data, wherein the type of each piece of news data comprises positive news data or negative news data, and the emotion value of each piece of news data is used for representing the influence degree of each piece of news data on the target service of the product to be analyzed;
and determining a first contribution degree of the plurality of pieces of news data to each public opinion target according to the type and the emotion value of target news data, wherein the target news data are news data containing each public opinion target in the plurality of pieces of news data.
In some possible embodiments of the present application, in determining the second contribution degree of the pieces of social media data to each of the public sentiments, the processing unit 402 is specifically configured to:
determining the type and emotion value of each piece of social media data in the plurality of pieces of social media data, wherein the type of each piece of social media data comprises positive social media data or negative social media data, and the emotion value of each piece of social media data is used for representing the influence degree of each piece of social media data on the target business of the product to be analyzed;
and determining a second contribution degree of the plurality of pieces of social media data to each public opinion target according to the type and the emotion value of target social media data, wherein the target social media data is the social media data containing each public opinion target in the plurality of pieces of social media data.
In some possible embodiments of the application, the public sentiment data are public sentiment data at any time, and the quantization index of each public sentiment target is the quantization index at any time; in an aspect of determining a target public opinion target of the at least one public opinion target according to the quantization index of each public opinion target, the processing unit 402 is specifically configured to:
determining a plurality of first variable quantities of the quantization index of each public opinion target in a plurality of preset time periods according to the quantization index of each public opinion target at any time;
acquiring a plurality of second variable quantities of the target service of the product to be analyzed in the preset time periods and a third variable quantity of the target service of the first analysis product in the preset time periods, wherein the product attribute of the first analysis product and the product attribute of the product to be analyzed are mutually exclusive;
determining a target public opinion target of the at least one public opinion target according to a plurality of first variable quantities of a quantization index of each public opinion target in a plurality of preset time periods, a plurality of second variable quantities of a target service of the product to be analyzed in the plurality of preset time periods, and a plurality of third variable quantities of the target service of the first analysis product in the plurality of preset time periods.
In some possible embodiments of the present application, when the influence of the change of the quantization index of each of the public opinion titles on the target business of the product to be analyzed is positive correlation, in terms of determining the target public opinion title of the at least one public opinion title according to a plurality of first variation amounts of the quantization index of each of the public opinion titles in a plurality of preset time periods, a plurality of second variation amounts of the target business of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target business of the first analysis product in the plurality of preset time periods, the processing unit 402 is specifically configured to:
determining a first preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the first preset time period is greater than a first threshold, the first threshold is greater than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is less than the first threshold;
determining a first number of the products to be analyzed, wherein the second variable quantity of the target business of the products to be analyzed in the first preset time period is larger than a second threshold value, and the second threshold value is larger than zero;
determining a second number of the first analysis products of which the third variation is larger than the second threshold value in the first preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
In some possible embodiments of the present application, when an influence of a change of a quantization index of each of the public opinion titles on a target service of the product to be analyzed is negative correlation, in an aspect of determining a target public opinion title of the at least one public opinion title according to a plurality of first variation amounts of the quantization index of each of the public opinion titles in a plurality of preset time periods, a plurality of second variation amounts of the target service of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target service of the first analysis product in the plurality of preset time periods, the processing unit 402 is specifically configured to:
determining a third preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the third preset time period is smaller than the opposite number of a first threshold, the first threshold is larger than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is smaller than the first threshold;
determining a first number of the products to be analyzed, wherein the second variation is smaller than the inverse number of a second threshold in a third preset time period, and the second threshold is larger than zero, according to the second variation of the target service of the products to be analyzed in the preset time periods;
determining a second number of the first analysis products of which the third variation is smaller than the inverse number of the second threshold value in the third preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 5, the electronic device 500 includes a transceiver 501, a processor 502, and a memory 503. Connected to each other by a bus 504. The memory 503 is used to store computer programs and data, and may transfer the data stored by the memory 503 to the processor 502.
The processor 502 is configured to read the computer program in the memory 503 to perform the following operations:
acquiring a plurality of public opinion data related to a product to be analyzed;
identifying the public opinion data to obtain at least one public opinion mark, wherein the at least one public opinion mark is determined according to the entity and the theme of the public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
In some possible embodiments of the present application, the public opinion data includes news data and social media data, and the processor 502 is specifically configured to perform the following operations in determining a quantization index of each of the public opinion targets according to the public opinion data:
determining a first contribution degree of the plurality of pieces of news data to each public opinion mark;
determining a second degree of contribution of the plurality of pieces of social media data to each of the public sentiments;
and determining the quantization index of each public opinion target according to the first contribution degree and the second contribution degree of each public opinion target.
In some possible embodiments of the present application, in determining the first contribution of the plurality of pieces of news data to each of the public opinion marks, the processor 502 is specifically configured to:
determining the type and emotion value of each piece of news data in the plurality of pieces of news data, wherein the type of each piece of news data comprises positive news data or negative news data, and the emotion value of each piece of news data is used for representing the influence degree of each piece of news data on the target service of the product to be analyzed;
and determining a first contribution degree of the plurality of pieces of news data to each public opinion target according to the type and the emotion value of target news data, wherein the target news data are news data containing each public opinion target in the plurality of pieces of news data.
In some possible embodiments of the present application, in determining the second contribution of the pieces of social media data to each of the public sentiments, the processor 502 is specifically configured to:
determining the type and emotion value of each piece of social media data in the plurality of pieces of social media data, wherein the type of each piece of social media data comprises positive social media data or negative social media data, and the emotion value of each piece of social media data is used for representing the influence degree of each piece of social media data on the target business of the product to be analyzed;
and determining a second contribution degree of the plurality of pieces of social media data to each public opinion target according to the type and the emotion value of target social media data, wherein the target social media data is the social media data containing each public opinion target in the plurality of pieces of social media data.
In some possible embodiments of the application, the public sentiment data are public sentiment data at any time, and the quantization index of each public sentiment target is the quantization index at any time; in determining a target public opinion target of the at least one public opinion target according to the quantization index of each public opinion target, the processor 502 is specifically configured to perform the following operations:
determining a plurality of first variable quantities of the quantization index of each public opinion target in a plurality of preset time periods according to the quantization index of each public opinion target at any time;
acquiring a plurality of second variable quantities of the target service of the product to be analyzed in the preset time periods and a third variable quantity of the target service of the first analysis product in the preset time periods, wherein the product attribute of the first analysis product and the product attribute of the product to be analyzed are mutually exclusive;
determining a target public opinion target of the at least one public opinion target according to a plurality of first variable quantities of a quantization index of each public opinion target in a plurality of preset time periods, a plurality of second variable quantities of a target service of the product to be analyzed in the plurality of preset time periods, and a plurality of third variable quantities of the target service of the first analysis product in the plurality of preset time periods.
In some possible embodiments of the present application, when the influence of the change of the quantization index of each of the public opinion titles on the target business of the product to be analyzed is positive correlation, in terms of determining the target public opinion title of the at least one public opinion title according to a plurality of first variation amounts of the quantization index of each of the public opinion titles in a plurality of preset time periods, a plurality of second variation amounts of the target business of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target business of the first analysis product in the plurality of preset time periods, the processor 502 is specifically configured to:
determining a first preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the first preset time period is greater than a first threshold, the first threshold is greater than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is less than the first threshold;
determining a first number of the products to be analyzed, wherein the second variable quantity of the target business of the products to be analyzed in the first preset time period is larger than a second threshold value, and the second threshold value is larger than zero;
determining a second number of the first analysis products of which the third variation is larger than the second threshold value in the first preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
In some possible embodiments of the present application, when an influence of a change in a quantization index of each of the public opinion titles on a target business of the product to be analyzed is negative correlation, in terms of determining a target public opinion title of the at least one public opinion title according to a plurality of first variation amounts of the quantization index of each of the public opinion titles in a plurality of preset time periods, a plurality of second variation amounts of the target business of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target business of the first analysis product in the plurality of preset time periods, the processor 502 is specifically configured to:
determining a third preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the third preset time period is smaller than the opposite number of a first threshold, the first threshold is larger than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is smaller than the first threshold;
determining a first number of the products to be analyzed, wherein the second variation is smaller than the inverse number of a second threshold in a third preset time period, and the second threshold is larger than zero, according to the second variation of the target service of the products to be analyzed in the preset time periods;
determining a second number of the first analysis products of which the third variation is smaller than the inverse number of the second threshold value in the third preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
Specifically, the transceiver 501 may be the obtaining unit 401 of the significance analyzing apparatus 400 according to the embodiment shown in fig. 4, and the processor 502 may be the processing unit 402 of the significance analyzing apparatus 400 according to the embodiment shown in fig. 4.
It should be understood that the electronic device in the present application may include a smart Phone (e.g., an Android Phone, an iOS Phone, a Windows Phone, etc.), a tablet computer, a palm computer, a notebook computer, a Mobile Internet device MID (MID), a wearable device, or the like. The above mentioned electronic devices are only examples, not exhaustive, and include but not limited to the above mentioned electronic devices. In practical applications, the electronic device may further include: intelligent vehicle-mounted terminal, computer equipment and the like.
Embodiments of the present application also provide a computer-readable storage medium, which stores a computer program, where the computer program is executed by a processor to implement part or all of the steps of any one of the significance analysis methods based on keyword recognition as described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any of the method for keyword recognition-based saliency analysis as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (10)

1. A significance analysis method based on keyword recognition is characterized by comprising the following steps:
acquiring a plurality of public opinion data related to a product to be analyzed;
identifying the public opinion data to obtain at least one public opinion mark, wherein the at least one public opinion mark is determined according to the entity and the theme of the public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
2. The method of claim 1, wherein the plurality of public opinion data comprises a plurality of news data and a plurality of social media data, and the determining the quantitative index of each public opinion target according to the plurality of public opinion data comprises:
determining a first contribution degree of the plurality of pieces of news data to each public opinion mark;
determining a second degree of contribution of the plurality of pieces of social media data to each of the public sentiments;
and determining the quantization index of each public opinion target according to the first contribution degree and the second contribution degree of each public opinion target.
3. The method of claim 2, wherein the determining the first contribution of the plurality of pieces of news data to each of the public sentiments comprises:
determining the type and emotion value of each piece of news data in the plurality of pieces of news data, wherein the type of each piece of news data comprises positive news data or negative news data, and the emotion value of each piece of news data is used for representing the influence degree of each piece of news data on the target service of the product to be analyzed;
and determining a first contribution degree of the plurality of pieces of news data to each public opinion target according to the type and the emotion value of target news data, wherein the target news data are news data containing each public opinion target in the plurality of pieces of news data.
4. The method of claim 2 or 3, wherein the determining the second contribution of the plurality of pieces of social media data to each of the public sentiments comprises:
determining the type and emotion value of each piece of social media data in the plurality of pieces of social media data, wherein the type of each piece of social media data comprises positive social media data or negative social media data, and the emotion value of each piece of social media data is used for representing the influence degree of each piece of social media data on the target business of the product to be analyzed;
and determining a second contribution degree of the plurality of pieces of social media data to each public opinion target according to the type and the emotion value of target social media data, wherein the target social media data is the social media data containing each public opinion target in the plurality of pieces of social media data.
5. The method according to any one of claims 1 to 4, wherein the pieces of public opinion data are pieces of public opinion data at any time, and the quantization index of each of the public opinion targets is the quantization index at the any time; the determining a target public opinion target of the at least one public opinion target according to the quantization index of each public opinion target comprises:
determining a plurality of first variable quantities of the quantization index of each public opinion target in a plurality of preset time periods according to the quantization index of each public opinion target at any time;
acquiring a plurality of second variable quantities of the target service of the product to be analyzed in the preset time periods and a third variable quantity of the target service of the first analysis product in the preset time periods, wherein the product attribute of the first analysis product and the product attribute of the product to be analyzed are mutually exclusive;
determining a target public opinion target of the at least one public opinion target according to a plurality of first variable quantities of a quantization index of each public opinion target in a plurality of preset time periods, a plurality of second variable quantities of a target service of the product to be analyzed in the plurality of preset time periods, and a plurality of third variable quantities of the target service of the first analysis product in the plurality of preset time periods.
6. The method of claim 5, wherein when the influence of the change of the quantitative index of each of the public sentiments on the target business of the product to be analyzed is positive correlation, the determining of the target public sentiment in the at least one public sentiment according to a plurality of first variation amounts of the quantitative index of each of the public sentiments in a plurality of preset time periods, a plurality of second variation amounts of the target business of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target business of the first product to be analyzed in the plurality of preset time periods comprises:
determining a first preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the first preset time period is greater than a first threshold, the first threshold is greater than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is less than the first threshold;
determining a first number of the products to be analyzed, wherein the second variable quantity of the target business of the products to be analyzed in the first preset time period is larger than a second threshold value, and the second threshold value is larger than zero;
determining a second number of the first analysis products of which the third variation is larger than the second threshold value in the first preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
7. The method of claim 5, wherein when an influence of a change in a quantitative indicator of each of the public sentiments on the target business of the product to be analyzed is negative correlation, the determining the target public sentiment in the at least one public sentiment according to a plurality of first variation amounts of the quantitative indicator of each of the public sentiments in a plurality of preset time periods, a plurality of second variation amounts of the target business of the product to be analyzed in the plurality of preset time periods, and a plurality of third variation amounts of the target business of the first product to be analyzed in the plurality of preset time periods comprises:
determining a third preset time period and a second preset time period in the plurality of preset time periods, wherein a first variation of the quantization index of each public opinion target in the third preset time period is smaller than the opposite number of a first threshold, the first threshold is larger than zero, and an absolute value of the first variation of the quantization index of each public opinion target in the second preset time period is smaller than the first threshold;
determining a first number of the products to be analyzed, wherein the second variation is smaller than the inverse number of a second threshold in a third preset time period, and the second threshold is larger than zero, according to the second variation of the target service of the products to be analyzed in the preset time periods;
determining a second number of the first analysis products of which the third variation is smaller than the inverse number of the second threshold value in the third preset time period according to the third variation of the target service of the first analysis product in the plurality of preset time periods;
determining a third number of the products to be analyzed, wherein the absolute value of the second variation of the target service in the second preset time period is greater than the second threshold, according to the second variation of the target service of the products to be analyzed in the plurality of preset time periods;
determining a fourth number of the first analysis products of which the absolute value of the third variation of the target service in the second preset time period is greater than the second threshold according to the third variation of the target service of the first analysis product in the preset time periods;
determining a target quantization index of each public opinion target according to the first number, the second number, the third number and the fourth number;
and taking the public sentiment target of which the target quantization index is in a preset interval as the target public sentiment target.
8. A significance analysis apparatus, comprising:
an acquisition unit for acquiring a plurality of pieces of public opinion data related to a product to be analyzed;
the processing unit is used for identifying the public opinion data to obtain at least one public opinion mark, and the at least one public opinion mark is determined according to the entity and the theme of the public opinion data;
determining a quantitative index of each public opinion target according to the public opinion data, wherein the quantitative index of each public opinion target is used for representing the influence degree of the public opinion target on a target service of the product to be analyzed, and the target service is any one service under the product to be analyzed;
and determining a target public opinion target in the at least one public opinion target according to the quantitative index of each public opinion target, wherein the influence of the target public opinion target on the target service has significance.
9. An electronic device, comprising: a processor coupled to the memory, and a memory for storing a computer program, the processor being configured to execute the computer program stored in the memory to cause the electronic device to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method according to any one of claims 1-7.
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