CN113779382A - Network public opinion prediction method based on microblog data - Google Patents

Network public opinion prediction method based on microblog data Download PDF

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CN113779382A
CN113779382A CN202110954872.1A CN202110954872A CN113779382A CN 113779382 A CN113779382 A CN 113779382A CN 202110954872 A CN202110954872 A CN 202110954872A CN 113779382 A CN113779382 A CN 113779382A
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刘定一
应毅
李晓明
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Abstract

The invention relates to the field of data analysis and prediction, in particular to a network public opinion prediction method based on microblog data, which is characterized by comprising the following steps: constructing a prediction model: the prediction model is a long-short term memory neural network model comprising two hidden layers, wherein the first hidden layer is a unidirectional long-short term memory neural network unit, and the second hidden layer is a bidirectional long-short term memory neural network unit; training a prediction model: calculating a moment in the time sequence according to the prediction model, and transmitting the current input and the output of the previous moment into the prediction model to obtain the current output as a predicted value; calculating errors according to the predicted values and the real values, performing back propagation solution through an optimizer, and updating model parameters until convergence; and calculating the total microblog popularity score based on microblog data, and inputting the hundredth index, the total microblog popularity score, the time offset and the output of the first hidden layer at the last moment as model input quantities into a trained prediction model to perform network public opinion prediction. The invention has high prediction accuracy.

Description

Network public opinion prediction method based on microblog data
Technical Field
The invention relates to the field of data analysis and prediction, in particular to a network public opinion prediction method based on microblog data.
Background
The internet is an important platform for the public to obtain information and express viewpoints, and the network plays a role in reflecting social sentiment and guiding public sentiment, but brings risks of social public sentiment events. The effective public opinion prediction method has necessary practical significance for predicting the development trend of network public opinions, resolving potential public opinion crisis and creating good network ecological environment. By predicting the trend of the network public sentiment in advance, the development situation of the hot event can be accurately judged, and reference is provided for relevant government departments to deal with the public sentiment crisis.
Influenced by a plurality of external factors, the development trend of network public sentiment has obvious ambiguity and uncertainty, and the artificial neural network has strong nonlinear fitting capability and is suitable for solving the problem of complex nonlinear time sequence data analysis. At present, since emerging expression forms such as media and mobile social platforms are emerging, the way and scale for generating and acquiring information by people are changed greatly, and real-time internet data (microblog, post and micro index) becomes a positive supplement for improving prediction accuracy.
Disclosure of Invention
The invention aims to provide a network public opinion prediction method based on microblog data, which is high in prediction accuracy.
In order to solve the technical problems, the technical scheme of the invention is as follows: a network public opinion prediction method based on microblog data comprises the following steps:
step 1: constructing a prediction model: defining a prediction model network structure, wherein the prediction model is a long short-term memory neural network model comprising two hidden layers, the first hidden layer is a unidirectional long short-term memory neural network unit, and the second hidden layer is a bidirectional long short-term memory neural network unit; the input of the prediction model is the input of the first hidden layer, and the output of the prediction model is the output of the second hidden layer; the input of the first hidden layer is a hundredth degree index, a total microblog hot degree score, a time offset and the output of the first hidden layer at the last moment; the input of the second hidden layer is the output of a hidden layer at the same moment and the output of the hidden layer at the same moment;
step 2: training a prediction model:
step 2.1: calculating a moment in the time sequence according to the prediction model, and transmitting the current input and the output of the previous moment into the prediction model to obtain the current output as a predicted value;
step 2.2: calculating errors according to the predicted values and the real values, performing back propagation solution through an optimizer, and updating model parameters;
step 2.3: repeating the step 2.1 and the step 2.2 until convergence;
and step 3: and calculating the total microblog popularity score based on microblog data, and inputting the hundredth index, the total microblog popularity score, the time offset and the output of the first hidden layer at the last moment as model input quantities into a trained prediction model to perform network public opinion prediction.
According to the scheme, the method for calculating the total microblog popularity score comprises the following steps:
analyzing microblog data of the network public sentiment event, collecting microblog hotspots according to keywords of the network public sentiment event, performing hotspot analysis on microblogs matched with the p keywords, and calculating microblog popularity scores, wherein the microblog popularity scores are obtained by accumulating the weights of forwarding numbers, comment numbers and praise numbers:
HotScoreiforwarding number + β comment number + γ vote number
Wherein, HotScoreiThe microblog popularity score of the ith keyword is represented, alpha represents the weight of the forwarding number of the ith keyword, beta represents the weight of the comment number of the ith keyword, and gamma represents the weight of the praise number of the ith keyword;
ranking the heat scores of the p keywords, and accumulating the top q keywords to obtain a total microblog heat score HotScore;
Figure BDA0003220080740000021
wherein q < p.
According to the scheme, the calculation method of the first hidden layer comprises the following steps:
Figure BDA0003220080740000022
wherein the content of the first and second substances,
Figure BDA0003220080740000023
an output, W, representing the first hidden layer at time t1Weight vector, BaidiIndex, representing the first hidden layertDenotes the Baidu index at the time t, and the Baidusndex is from Baidu website, HotScoretThe total microblog popularity score at the time T is represented, delta T represents a time offset, and the time offset refers to a time interval between a predicted day and the first day of a public sentiment event; σ represents an activation function, which is a Sigmoid function.
According to the scheme, the calculation method of the second hidden layer comprises the following steps:
Figure BDA0003220080740000024
wherein the content of the first and second substances,
Figure BDA0003220080740000025
an output, W, representing the second hidden layer at time t2A weight matrix representing the second hidden layer,
Figure BDA0003220080740000026
representing the output at the instant t-1 of the second hidden layer,
Figure BDA0003220080740000027
representing the input vector from the first hidden layer to the second hidden layer at time t.
According to the scheme, in the training process, the error index of the prediction model is a loss function:
the loss function is the sum of the square sum of the prediction error and the square sum of the model weight parameter, and the specific formula is as follows:
Figure BDA0003220080740000028
wherein n is the number of samples, h (x)i) Representing input samples xiPredicted output of time model, yiIs a sample xiTrue value of (m) is the model weightThe number of the first and second groups is,
Figure BDA0003220080740000029
represents the square of the jth weight, α represents the learning rate, and α takes 0.1.
According to the scheme, in the step 1, when the prediction model network structure is defined, the rejection rate of each layer network node is set to be 0.2, and an optimizer is set to estimate Adam by using the adaptive moment.
The invention has the following beneficial effects:
according to the invention, the characteristic of small public opinion data volume is considered, the designed prediction model is composed of two hidden layers of a unidirectional long-short term memory neural network unit and a bidirectional long-short term memory neural network unit, the risk of overfitting caused by less training samples is reduced while the long-short term memory neural network characteristic is kept, meanwhile, social media information, namely microblog data is used as one of the inputs of model calculation, the improvement is carried out from the two aspects of prediction model and data expansion, the network public opinion prediction method based on microblog data is provided, the network public opinion development trend prediction is carried out by combining real-time microblog data and authoritative Baidu indexes, and the prediction precision is effectively improved.
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FIG. 1 is a schematic diagram of a network architecture of a prediction model according to the present invention;
FIG. 2 is a schematic diagram of a long term short term memory neural network according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Referring to fig. 1 and 2, the present invention provides a method for predicting internet public sentiment based on microblog data, which includes:
step 1: constructing a prediction model: defining a prediction model network structure, setting the rejection rate of each layer network node to be 0.2, and setting an optimizer to be adaptive moment estimation Adam;
on the basis of a traditional Long Short-Term Memory neural network (LSTM), a Long Short-Term Memory neural network model comprising two hidden layers is constructed as a prediction model, the first hidden layer is a unidirectional Long Short-Term Memory neural network unit, and the second hidden layer is a bidirectional Long Short-Term Memory neural network unit; the input of the prediction model is the input of the first hidden layer, and the output of the prediction model is the output of the second hidden layer; the input of the first hidden layer is a hundredth degree index, a total microblog hot degree score, a time offset and the output of the first hidden layer at the last moment; the input of the second hidden layer is the output of a hidden layer at the same moment and the output of the hidden layer at the same moment;
the method for calculating the total microblog popularity score comprises the following steps:
firstly, analyzing microblog data of a network public sentiment event, collecting microblog hotspots according to keywords of the network public sentiment event, performing hotspot analysis on microblogs matched with p keywords, and calculating microblog popularity scores, wherein the microblog popularity scores are obtained by accumulating the weights of forwarding numbers, comment numbers and praise numbers:
HotScoreiα + β + γ + praise (1)
Wherein, HotScoreiThe microblog popularity score of the ith keyword is represented, alpha represents the weight of the forwarding number of the ith keyword, beta represents the weight of the comment number of the ith keyword, and gamma represents the weight of the praise number of the ith keyword;
ranking the heat scores of the p keywords, and accumulating the top q keywords to obtain a total microblog heat score HotScore;
Figure BDA0003220080740000031
wherein q < p; in this example, p is 50 and q is 10.
The calculation method of the first hidden layer comprises the following steps:
Figure BDA0003220080740000041
wherein the content of the first and second substances,
Figure BDA0003220080740000042
an output, W, representing the first hidden layer at time t1Weight vector, BaidiIndex, representing the first hidden layertDenotes the Baidu index at the time t, and the Baidusndex is from Baidu website, HotScoretThe total microblog popularity score at the time T is represented, delta T represents a time offset, and the time offset refers to a time interval between a predicted day and the first day of a public sentiment event; σ represents an activation function, which is a Sigmoid function.
The calculation method of the second hidden layer comprises the following steps:
Figure BDA0003220080740000043
wherein the content of the first and second substances,
Figure BDA0003220080740000044
an output, W, representing the second hidden layer at time t2A weight matrix representing the second hidden layer,
Figure BDA0003220080740000045
representing the output at the instant t-1 of the second hidden layer,
Figure BDA0003220080740000046
representing the input vector from the first hidden layer to the second hidden layer at time t.
Referring to fig. 1, the first hidden layer and the second hidden layer include LSTM storage units, and referring to fig. 2, the principle of the LSTM units is as follows:
the 1 LSTM memory unit mainly comprises an input gate (input gate), an output gate (output gate) and a forgetting gate (forget gate);
the calculation formula for the LSTM unit is as follows:
it＝σ(Wi[ht-1,xt])
ft＝σ(Wf[ht-1,xt])
zt＝tanh(Wz[ht-1,xt])
ct＝ft·ct-1+it·zt
ot＝σ(Wo[ht-1,xt])
ht＝ot·tanh(ct)
in the formula: i.e. itIs an input gate; f. oftTo forget the door; otIs an output gate; σ denotes an activation function, usually Sigmoid; w is a weight matrix of each neural network layer; x is the number oftIs the input value at the current moment; h ist-1Receiving the output value of the last moment at the current moment t; c. Ct-1Is the state value at the time t-1; z is a radical oftThe candidate state value at the current moment is obtained; c. CtThe state value of the current moment; h istIs the output value at the current moment.
Step 2: training a prediction model:
step 2.1: calculating a moment in the time sequence according to the prediction model, and transmitting the current input and the output of the previous moment into the prediction model to obtain the current output as a predicted value;
step 2.2: calculating errors according to the predicted values and the real values, performing back propagation solution through an optimizer, and updating model parameters;
in the training process, the error index of the prediction model is a loss function, namely, the error between the predicted value and the true value (label value) is calculated by using the loss function:
the loss function is the sum of the square sum of the prediction error and the square sum of the model weight parameter, and the specific formula is as follows:
Figure BDA0003220080740000051
wherein n is the number of samples, h (x)i) Representing input samples xiPredicted output of time model, yiIs a sample xiM is the number of model weights,
Figure BDA0003220080740000052
representing the jth weightThe square, α, represents the learning rate, and α is 0.1.
Step 2.3: repeating the step 2.1 and the step 2.2 until convergence;
and step 3: and calculating the total microblog popularity score based on microblog data, and inputting the hundredth index, the total microblog popularity score, the time offset and the output of the first hidden layer at the last moment as model input quantities into a trained prediction model to perform network public opinion prediction.
One specific example is given below:
the hot spot events of 'Chongqing Bao Jie girl owner event' (7 month 30-8 month 14 days), '996 work system event' (4 month 11-4 month 26 days), 'black hole photo first event' (4 month 8-4 month 23 days) 3 which occur in 2019 are taken as training samples, and parameters are optimized by using a training sample training model.
The test sample is used as a test sample, namely a college school companion event (7-12 th 7-27 th 2019), and the validity and the accuracy of the model are verified by using the test sample.
The experimental data mainly include: hundredth index, microblog popularity score and time offset.
When the model is trained, inputting the hundredth index of the first day, the microblog popularity score and the time offset (namely 0), calculating the hundredth index of the next day, comparing the calculation result with the true value of the hundredth index to adjust the model parameters, and repeating the steps.
During model test, only the hundredth index of the first day and the microblog popularity score of each day are given, the time offset is increased from 0, and the hundredth index of the 2 nd day to the 16 th day is measured.
The data of the results of the model calculations are shown in the table below.
Figure BDA0003220080740000053
The method is based on the traditional long-short term memory neural network (LSTM), a long-short term memory neural network model comprising two hidden layers is constructed, the first hidden layer is a unidirectional long-short term memory neural network unit, the second hidden layer is a bidirectional long-short term memory neural network unit, microblog data is used as model input, quantitative prediction of the sentiment public opinion event popularity index is carried out, and the prediction accuracy is high.
The parts not involved in the present invention are the same as or implemented using the prior art.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (6)

1. A network public opinion prediction method based on microblog data is characterized by comprising the following steps: comprises that
Step 1: constructing a prediction model: defining a prediction model network structure, wherein the prediction model is a long short-term memory neural network model comprising two hidden layers, the first hidden layer is a unidirectional long short-term memory neural network unit, and the second hidden layer is a bidirectional long short-term memory neural network unit; the input of the prediction model is the input of the first hidden layer, and the output of the prediction model is the output of the second hidden layer; the input of the first hidden layer is a hundredth degree index, a total microblog hot degree score, a time offset and the output of the first hidden layer at the last moment; the input of the second hidden layer is the output of a hidden layer at the same moment and the output of the hidden layer at the same moment;
step 2: training a prediction model:
step 2.1: calculating a moment in the time sequence according to the prediction model, and transmitting the current input and the output of the previous moment into the prediction model to obtain the current output as a predicted value;
step 2.2: calculating errors according to the predicted values and the real values, performing back propagation solution through an optimizer, and updating model parameters;
step 2.3: repeating the step 2.1 and the step 2.2 until convergence;
and step 3: and calculating the total microblog popularity score based on microblog data, and inputting the hundredth index, the total microblog popularity score, the time offset and the output of the first hidden layer at the last moment as model input quantities into a trained prediction model to perform network public opinion prediction.
2. The microblog-data-based online public opinion prediction method according to claim 1, characterized in that: the method for calculating the total microblog popularity score comprises the following steps:
analyzing microblog data of the network public sentiment event, collecting microblog hotspots according to keywords of the network public sentiment event, performing hotspot analysis on microblogs matched with the p keywords, and calculating microblog popularity scores, wherein the microblog popularity scores are obtained by accumulating the weights of forwarding numbers, comment numbers and praise numbers:
HotScoreiforwarding number + β comment number + γ vote number
Wherein, HotScoreiThe microblog popularity score of the ith keyword is represented, alpha represents the weight of the forwarding number of the ith keyword, beta represents the weight of the comment number of the ith keyword, and gamma represents the weight of the praise number of the ith keyword;
ranking the heat scores of the p keywords, and accumulating the top q keywords to obtain a total microblog heat score HotScore;
Figure FDA0003220080730000011
wherein q < p.
3. The microblog-data-based online public opinion prediction method according to claim 1, characterized in that: the calculation method of the first hidden layer comprises the following steps:
Figure FDA0003220080730000012
wherein the content of the first and second substances,
Figure FDA0003220080730000013
to representOutput of the first hidden layer at time t, W1Weight vector, BaidiIndex, representing the first hidden layertDenotes the Baidu index at the time t, and the Baidusndex is from Baidu website, HotScoretThe total microblog popularity score at the time T is represented, the delta T represents a time offset, and the time offset refers to a time interval between a predicted day and the first day of a public sentiment event; σ represents an activation function, which is a Sigmoid function.
4. The microblog-data-based online public opinion prediction method according to claim 1, characterized in that: the calculation method of the second hidden layer comprises the following steps:
Figure FDA0003220080730000021
wherein the content of the first and second substances,
Figure FDA0003220080730000022
an output, W, representing the second hidden layer at time t2A weight matrix representing the second hidden layer,
Figure FDA0003220080730000023
representing the output at the instant t-1 of the second hidden layer,
Figure FDA0003220080730000024
representing the input vector from the first hidden layer to the second hidden layer at time t.
5. The microblog-data-based online public opinion prediction method according to claim 1, characterized in that: in the training process, the error index of the prediction model is a loss function:
the loss function is the sum of the square sum of the prediction error and the square sum of the model weight parameter, and the specific formula is as follows:
Figure FDA0003220080730000025
wherein n is the number of samples, h (x)i) Representing input samples xiPredicted output of time model, yiIs a sample xiM is the number of model weights,
Figure FDA0003220080730000026
represents the square of the jth weight, α represents the learning rate, and α takes 0.1.
6. The microblog-data-based online public opinion prediction method according to claim 1, characterized in that: in step 1, when a prediction model network structure is defined, the rejection rate of each layer of network nodes is set to be 0.2, and an optimizer is set to be adaptive moment estimation Adam.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114742281A (en) * 2022-03-21 2022-07-12 西北工业大学 Public event network public opinion popularity prediction method based on grey model

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114742281A (en) * 2022-03-21 2022-07-12 西北工业大学 Public event network public opinion popularity prediction method based on grey model

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