CN114742281A - Public event network public opinion popularity prediction method based on grey model - Google Patents

Public event network public opinion popularity prediction method based on grey model Download PDF

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CN114742281A
CN114742281A CN202210277662.8A CN202210277662A CN114742281A CN 114742281 A CN114742281 A CN 114742281A CN 202210277662 A CN202210277662 A CN 202210277662A CN 114742281 A CN114742281 A CN 114742281A
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黄攀飞
邓正宏
徐会军
陈雪丽
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Abstract

The invention discloses a public event network public opinion popularity prediction method based on a gray model, which comprises the following steps: s1, data collection operation is carried out by utilizing collector software; s2, preprocessing the collected data and calculating a heat trend value by using a model; s3, randomly selecting 500 pieces of data from the collected data to analyze, and judging the emotional tendency trend of the current day; and S4, putting the heat trend value into a prediction model for analysis, and predicting the trend of the heat trend. The invention realizes the prediction of public event network public opinion popularity by using the gray model.

Description

Public event network public opinion popularity prediction method based on grey model
Technical Field
The invention is applied to the field of public opinion popularity prediction, and particularly relates to a public affair online public opinion popularity prediction method based on a gray model.
Background
The research focus of China on the popularity of the network public sentiment mainly focuses on multiple aspects of the construction of an evaluation index system of a hotspot event, the trend prediction of the popularity of a topic, the discovery of the hotspot topic, sentiment analysis and the like, and enterprises and research units determine the popularity evaluation index of the network public sentiment from the viewpoint of organically combining the qualitative and quantitative aspects. In the public sentiment popularity index system constructed in China, students mainly give rights around news events, netizens, clicks, post transfer amount and other indexes, and comprehensively evaluate the popularity of topics and predict the trend by using different technical means. How to accurately predict the public network opinion popularity of the public events becomes a technical problem to be solved in the field.
Disclosure of Invention
The invention aims to solve the technical problem of providing a public event network public opinion popularity prediction method based on a gray model aiming at the defects of the prior art.
In order to solve the technical problems, the public event network public opinion popularity prediction method based on the gray model comprises the following steps:
s1, data collection operation is carried out by utilizing collector software;
s2, preprocessing the collected data and calculating a heat trend value by using a model;
s3, randomly selecting 500 pieces of data from the collected data for analysis, and judging the emotional tendency trend of the day;
and S4, putting the heat trend value into a prediction model for analysis, and predicting the trend of the heat trend. As a possible implementation manner, further, the step S2 includes the following specific steps:
s21, removing duplicate of the collected data according to the Bo Wen, and filling the number '0' of the praise, the comment number and the forwarding number of the Bo Wen;
s22, carrying out weight calculation on the data after the de-weight filling;
s23, calculating the heat trend value of the day according to the weights of the praise number, the comment number and the forwarding number; using the calculation formula: h (i) ═ Ai*W1+Bi*W2+Ci*W3And (6) performing calculation.
As a possible implementation manner, further, the step S22 includes the following specific steps:
s221, carrying out standardization processing on the data by adopting a forward index formula;
Figure BDA0003556621630000021
s222, calculating the specific gravity of a certain index in a certain day by the following formula:
Figure BDA0003556621630000022
s223, carrying out entropy calculation of a certain index, wherein the formula is as follows:
Figure BDA0003556621630000023
s224, carrying out information entropy redundancy calculation of a certain index, wherein the formula is as follows: dj＝1-ej；
S225, index weight calculation is carried out, and the formula is as follows:
Figure BDA0003556621630000024
as a possible implementation manner, further, the step S3 includes the following specific steps: data preprocessing, emotion value calculation operation and experimental result output operation.
As a possible implementation manner, further, the data preprocessing operation specifically includes:
s31, removing stop words from the blog;
s32, Chinese word segmentation is carried out and classified into emotional words, negative words and degree adverbs.
As a possible implementation manner, further, the emotion value calculation operation specifically includes:
s33, finding out corresponding weight in corresponding emotion word stock;
and S34, calculating the emotion value according to the weights in the emotional words, the negative words and the degree adverbs in the blog text and judging the emotional tendency of the blog text.
As a possible implementation manner, further, the step S4 includes the following specific steps:
s41, accumulating the heat trend values for one time to generate a sequence;
s42, calculating an adjacent mean value generation sequence;
s43, constructing a data matrix and a data vector;
s44, calculating a development coefficient and an ash action amount;
s45, obtaining a time response function and predicting according to the function;
and S46, comparing the result obtained according to the time response function with the actual result, and if the result meets the model accuracy grade condition, passing the prediction, otherwise failing the prediction.
By adopting the technical scheme, the invention has the following beneficial effects: the method is used for constructing the microblog public opinion heat trend value model. And finally, calculating a public opinion popularity trend value according to the weight values, mainly calculating the popularity trend value of the microbo public opinion event, and providing a basis for constructing a GM model. And analyzing microblog public sentiment based on the BosonNLP sentiment dictionary. Chinese word segmentation is carried out on microblog messages and is divided into three categories of emotion words, negative words and degree adverbs, then emotion dictionaries are respectively matched to obtain corresponding emotion scores, and finally the microblog messages are divided into three categories of positive, negative and neutral according to the counted emotion score values. And predicting the popularity of the network public sentiment based on the GM model. The short-term prediction analysis is carried out on the trend value of the microblog public opinion popularity heat based on the GM model principle and the analysis of the model inspection method, and the result shows that the model precision has an obvious effect.
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The invention is described in further detail below with reference to the following figures and embodiments:
FIG. 1 is a flowchart illustrating step S3 according to the present invention;
FIG. 2 is a flowchart of step S4 according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be described clearly and completely in the following with reference to the accompanying drawings.
As shown in fig. 1-2, the present invention provides a public event internet public opinion heat prediction method based on a gray model, comprising the following steps:
s1, data collection operation is carried out by utilizing collector software; the preferred octopus collector is an octopus collector, and the octopus collector is a web crawler tool with simple operation and strong functions. According to the method, the 'octopus ocellatus collector' software is used, and the 'xx event' is used as a search keyword, original microblog blog text contents in the period from 4 month 1 to 4 month 30 in 2020 to the Xinlang microblog website are collected, and corresponding information such as the number of praise microblogs, the number of comments on microblogs, the number of forwarded microblogs and the like is collected, so that 604865 pieces of blog text are counted.
S2, preprocessing the collected data and calculating a heat trend value by using a model; the step S2 specifically includes:
s21, removing duplicate of the collected data according to the Bo Wen, and filling the number '0' of the praise, the comment number and the forwarding number of the Bo Wen;
s22, carrying out weight calculation on the data after the weight removal and filling; further, the specific step of step S22 includes:
s221, carrying out standardization processing on the data by adopting a forward index formula;
Figure BDA0003556621630000051
s222, calculating the specific gravity of a certain index in a certain day by the following formula:
Figure BDA0003556621630000052
s223, carrying out entropy calculation of a certain index, wherein the formula is as follows:
Figure BDA0003556621630000053
s224, carrying out information entropy redundancy calculation of a certain index, wherein the formula is as follows: dj＝1-ej；
S225, index weight calculation is carried out, and the formula is as follows:
Figure BDA0003556621630000054
s23, calculating the heat trend value of the day according to the weights of the praise number, the comment number and the forwarding number; using the calculation formula: h (i) ═ Ai*W1+Bi*W2+Ci*W3And (6) performing calculation.
S3, randomly selecting 500 pieces of data from the collected data for analysis, and judging the emotional tendency trend of the day; the method comprises the following specific steps: data preprocessing, emotion value calculation operation and experimental result output operation. Further, the data preprocessing operation specifically includes:
s31, removing stop words from the blog;
s32, performing Chinese word segmentation operation and classifying the words into sentiment words, negative words and degree adverbs.
Further, the emotion value calculation operation specifically includes:
s33, finding out corresponding weight in corresponding emotion word stock;
and S34, calculating the emotion value according to the weights in the emotional words, the negative words and the degree adverbs in the blog text and judging the emotional tendency of the blog text.
The specific operations include, for example: part of "i am happy and happy today" is that the word segmentation can be obtained directly by removing the characters that are not used: [ 'very', 'happy', 'very', 'happy' ]. Assume that the onset weight W is 1 and the emotion score is 0.
The first emotion word is happy, and because the emotion weight of the happy emotion is 1.4895, the emotion score is W and the emotion weight is 1.4895; then, a gap occurs between happy mood and the beginning of the next emotional word, the degree of which is 1.8, and thus the weight W is 1 × 1.8 — 1.8.
Then, the next emotion word, i.e. the word is open, the emotion weight is 2.6123, the weight W is 1.8, the emotion score is score +1.8 × 2.6123 is 6.1917, and the traversal is finished.
And S4, putting the heat trend value into a prediction model for analysis, and predicting the trend of the heat trend.
The method comprises the following specific steps:
s41, accumulating the heat trend values for one time to generate a sequence;
s42, calculating an adjacent mean value generation sequence;
s43, constructing a data matrix and a data vector;
s44, calculating a development coefficient and an ash action amount;
s45, obtaining a time response function and predicting according to the function;
and S46, comparing the result obtained according to the time response function with the actual result, and if the result meets the model precision grade condition, the prediction is passed, otherwise, the prediction fails.
The calculation in the operation process is as follows:
definition 4.1 setting X(0)As the original sequence:
X(0)＝(x(0)(1),x(0)(2),...,x(0)(n))
X(1)is X(0)The sequence of one summation of (a), we note as 1-AGO sequence:
X(1)＝(x(1)(1),x(1)(2),...,x(1)(n))
wherein the content of the first and second substances,
Figure BDA0003556621630000071
Z(1)is X(1)The sequence generated by the close-to-average of (1):
Z(1)＝(z(1)(2),z(1)(3),...,z(1)(n))
wherein
Figure BDA0003556621630000072
Definition 4.2 setting X(0)＝(x(0)(1),x(0)(2),...,x(0)(n)), meterCalculating to obtain X(1)Is X(0)One accumulation of (a) to generate a sequence of:
X(1)＝(x(1)(1),x(1)(2),...,x(1)(n))
then
x(0)(k)+ax(1)(k)＝b
In the original form of the GM (1,1) model, a, b are the parameters to be distinguished.
Definition 4.3 setting X(0)＝(x(0)(1),x(0)(2),...,x(0)(n))，X(1)Is X(0)A sequence generated by a single accumulation of Z(1)Is X(1)The sequence generated by the close-to-average of (1):
X(1)＝(x1(1),x1(2),...,x1(n))
Z(1)＝(z(1)(2),z(1)(3),...,z(1)(n))
then we give the basic form of the GM (1,1) model as follows:
x(0)(k)+az(1)(k)＝b
definition 4.4 method according to matrix, we note the parameter columns as
Figure BDA0003556621630000081
And recording the data series B and the data vector Y as follows:
Figure BDA0003556621630000082
the GM (1,1) model can be expressed as
Figure BDA0003556621630000083
The values of a and b are obtained by following the principle of unary linear regression, i.e. least squares, and there are
Figure BDA0003556621630000084
Definition 4.5 setting X(0)＝(x(0)(1),x(0)(2),...,x(0)(n))，X(1)Is X(0)A sequence generated by a single accumulation of Z(1)Is X(1)The sequence generated by the close-to-average of (1):
X(1)＝(x1(1),x1(2),...,x1(n))
Z(1)＝(z(1)(2),z(1)(3),...,z(1)(n))
then we differentiate the form:
Figure BDA0003556621630000085
is referred to as x(0)(k)+az(1)(k) B whitening equation.
The theorem 4.1 sets forth the ratios of B, Y,
Figure BDA0003556621630000086
as defined in definition 2.4
Figure BDA0003556621630000087
Then
(1) Whitening equation
Figure BDA0003556621630000088
Is solved as
Figure BDA0003556621630000089
(2) The time response function sequence of the GM (1,1) model is
Figure BDA00035566216300000810
Where k is 1, 2.
(3) Reduction number
Figure BDA00035566216300000811
Where k is 1, 2.
In the expression, the parameter a is
Figure BDA00035566216300000812
Is composed of
Figure BDA00035566216300000813
The parameter b is the gray effect amount, and reflects the information of the background value.
Let X(0)＝(x(0)(1),x(0)(2),......,x(0)(n)) is an original sequence, and the simulated value of the original sequence obtained according to the GM (1,1) model is recorded as:
Figure BDA0003556621630000091
then the variance of the original sequence is:
Figure BDA0003556621630000092
wherein
Figure BDA0003556621630000093
Let the residual sequence E ═ (E (1), E (2),. E (n)), where
Figure BDA0003556621630000094
Figure BDA0003556621630000095
Then the variance of the residual is
Figure BDA0003556621630000096
In the above formula, i is 1, 2. Then we define the posterior difference ratio as C, then
Figure BDA0003556621630000097
And finally, judging the precision of the model according to the posterior difference ratio C in the table 1.
TABLE 1 model accuracy rating
Figure BDA0003556621630000098
Calculation of the specific embodiment:
(1) setting the trend value of microblog public opinion popularity at 1 day 4 month to 10 days 4 month as an original sequence X(0)，
X(0)＝[1167758，475332，785852，332917，529918，350850，174727， 336278，244218，241390]
(2) For original data X(0)Performing one-time accumulation to generate a sequence X(1)The following can be obtained:
X(1)＝[1167758，1643090，2428942，2761859，3291777，3642627，38 17354，4153632，4397850，4639240]
(3) constructing a data series B and a data vector Y:
Figure BDA0003556621630000101
Figure BDA0003556621630000102
(4) determining parameters a and b by using a least square method:
[a,b]T＝(BTB)-1BTY＝[0.13,795795]T
(5) the time response sequence of the GM (1,1) model is:
Figure BDA0003556621630000103
(6) calculating a posterior difference ratio C for precision test:
Figure BDA0003556621630000104
Figure BDA0003556621630000105
Figure BDA0003556621630000106
the foregoing is directed to embodiments of the present invention, and equivalents, modifications, substitutions and variations may be made by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A public event network public opinion popularity prediction method based on a gray model is characterized by comprising the following steps:
s1, data collection operation is carried out by utilizing collector software;
s2, preprocessing the collected data and calculating a heat trend value by using a model;
s3, randomly selecting 500 pieces of data from the collected data to analyze, and judging the emotional tendency trend of the current day;
and S4, putting the heat trend value into a prediction model for analysis, and predicting the trend of the heat trend.
2. The public event internet public opinion popularity prediction method based on the gray model as claimed in claim 1, wherein: the step S2 includes the following steps:
s21, removing duplicate of the collected data according to the Bo Wen, and filling the number '0' of the praise, the comment number and the forwarding number of the Bo Wen;
s22, carrying out weight calculation on the data after the de-weight filling;
s23, calculating the heat trend value of the day according to the weights of the praise number, the comment number and the forwarding number; using the calculation formula: h (i) ═ Ai*W1+Bi*W2+Ci*W3And (6) performing calculation.
3. The public event internet public opinion popularity prediction method based on gray model as claimed in claim 2, characterized in that: the step S22 includes the following steps:
s221, carrying out standardization processing on the data by adopting a forward index formula;
Figure FDA0003556621620000021
s222, calculating the specific gravity of a certain index in a certain day by the following formula:
Figure FDA0003556621620000022
s223, carrying out entropy calculation of a certain index, wherein the formula is as follows:
Figure FDA0003556621620000023
s224, carrying out information entropy redundancy calculation of a certain index, wherein the formula is as follows: dj＝1-ej；
S225, index weight calculation is carried out, and the formula is as follows:
Figure FDA0003556621620000024
4. the public event network public opinion popularity prediction method based on the gray model as claimed in claim 1, characterized in that: the specific step of step S3 includes: data preprocessing, emotion value calculation operation and experimental result output operation.
5. The public event network public opinion popularity prediction method based on gray model as claimed in claim 4, wherein: the data preprocessing operation specifically comprises:
s31, removing stop words from the blog;
s32, Chinese word segmentation is carried out and classified into emotional words, negative words and degree adverbs.
6. The public event network public opinion popularity prediction method based on the gray model as claimed in claim 4, characterized in that: the emotion value calculation operation specifically includes:
s33, finding out corresponding weight in corresponding emotion word stock;
and S34, calculating the emotion value according to the weights in the emotional words, the negative words and the degree adverbs in the blog text and judging the emotional tendency of the blog text.
7. The public event internet public opinion popularity prediction method based on the gray model as claimed in claim 1, wherein: the step S4 includes the following steps:
s41, accumulating the heat trend values for one time to generate a sequence;
s42, calculating an adjacent mean value generation sequence;
s43, constructing a data matrix and a data vector;
s44, calculating a development coefficient and an ash action amount;
s45, obtaining a time response function and predicting according to the function;
and S46, comparing the result obtained according to the time response function with the actual result, and if the result meets the model precision grade condition, the prediction is passed, otherwise, the prediction fails.
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