# Parameter Optimization of Educational Network Ecosystem Based on BERT Deep Learning Model

Authors: Sha Tao
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The key sentimental words in the text cannot be paid attention to effectively, and language knowledge such as the text information and the sentimental resources are relied on. Therefore, it is necessary to make full use of this unique sentimental information to achieve the best performance of the model. In order to solve the problems, a method based on the fusion of the convolutional neural network and the bidirectional GRU network text sentiment analysis capsule model to analyze the ideological and political education of public opinion is put forward. In this model, each sentiment category is combined with the attention mechanism to generate feature vectors to construct sentiment capsules. Finally, the text sentiment categories are judged according to the attributes of the capsules. The model is tested on MR, IMDB, SST-5, and the data set of the ideological and political education review. Experimental results show that compared with MC-CNN-LSTM, the readiness rate of the proposed model is improved by 5.1%, 2.8%, 2.8%, and 1.6% on four public Chinese and English data sets, respectively. Compared with LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, the accuracy of the proposed MC-BiGRU-Capsule model on MR and SST-5 data are 3.2%, 2.4%, and 3.4% higher than that of the LR-Bi-LSTM, NSCL, and multi-Bi-LSTM models, respectively. It also shows a better classification effect on multiclassification data sets. It is concluded that compared with other baseline models, this method has a better classification effect.


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## Body

## 1. Introduction

We all know that the new media represented by Weibo, WeChat, and QQ is a double-edged sword that shows the advantages of both pros and cons. New media technologies allow for “freedom and direct expression” and “everyone has a microphone.” All kinds of public opinion in the era of microphone floods, including educational public opinions, can be discussed and spread, which influences the social weather vane. Public perception of education refers to how ordinary people talk about education, including primary and secondary education, public opinion, good advice, anger, and rage. From the inclusion of education policy in the action plan to the formulation, implementation, evaluation, and even evaluation of it, public opinion has always been at the forefront. Today, we increasingly recognize the close connection between education, public opinion, and education policy [1].In the era of “everyone has a microphone,” the wind begins to blow from the grass. Most people are overly concerned with the decision-making process in formulating, implementing, evaluating, and finalizing education policies, which is easy to cause neglect to other content. That is why some problems can be elevated to policy issues while others are excluded. How to detect and respond to the crisis of public opinion in a complex environment of public opinion is inextricably linked to the collection, study, evaluation, guidance, and intervention of public opinion in education. A multifaceted and systematic study of public opinion in education [2]. The Fourth Plenary Session of the 16th CPC National People’s Congress made it clear that China needs to gradually establish a comprehensive system for collecting and analyzing public opinion in order to ensure that it can further reflect public opinion. The Sixteenth Plenary Session of the 16th Central Committee reiterated its importance [3]. The Ministry of Education, in conducting a spiritual survey of the 6th Plenary Session of the 17th Central Committee, called for the development of China’s future education as a starting point, and the gradual establishment of a system that reflects scientific and standardized public opinion to provide timely and accurate public opinion.Providing educational products and services is the main goal of developing educational informatization enterprises. From the perspective of the development of educational informatization enterprises as a whole, the sustainability of its products and services will be affected by the whole enterprise’s ecological environment. Therefore, in order to achieve sustainable development of educational information products and services, it is first necessary to clarify the logical relationship between the elements of the educational information product and service system. Therefore, we need to start with the entire enterprise ecosystem in order to identify the key elements of the educational information product and service ecosystem and its interaction mechanisms. The service ecosystem model is shown in Figure1.Figure 1
Educational informatization products and service ecosystem model.


## 2. Literature Review

Larson et al. proposed the application of the “survival of the fittest” principle to maintain the balance of the resource pool, establish a convenient feedback mechanism, promote the coevolution of the resource pool, promote the flow of information in the resource pool, and improve the utilization rate of resources [4]. From the perspective of basic knowledge of computer networks and ecology, Wu studied the network ecosystem, analyzed the structure, characteristics, and attributes of the ecosystem [5], and put forward some new concepts for a network system. Through the study of some network behavior of the network (including the competition between different network technologies or software), based on the model of population ecology, the competition model between network populations was constructed, and the software was used to simulate it, so as to provide a reference for the optimization and allocation of network resources. Deng studied the demands of administrators, users, and teaching supervisors for online educational resources by applying the EPSS concept to the educational resource management. The research tried to integrate the EPSS technology with educational resource management to construct an electronic performance support system for learning resource centers for the practical application of network teaching. Through the framework of the online education resource integration, the distributed education resource, database, and education resource integration service center were organically interconnected, forming a co-construction and sharing mode of online education resources with intensive management and distributed storage, thus achieving the best solution of resource construction, resource sharing, and resource application [6]. Lu proposed the integration of online education resources based on three kinds of social software: Blog, Wiki, and Model, and analyzed their respective integration methods, strategies, and characteristics [7]. Tang and T discussed the integration and integration modes of online education resources and put forward three integration modes, namely, the education resource management database mode, the education resource center mode, and the distributed education resource network mode. There are three aspects of educational resource sharing in the network environment: self-sharing, sharing with others, and global educational resource sharing [8]. Aydn used a k-means clustering algorithm to cluster online public opinions as topics, so as to monitor public opinions [9]. In the context of the big data era, Guo and M. used the Mahout text algorithm to mine the online public opinion information [10]. Ferguson analyzed enterprise public opinions with the sentiment dictionary method and achieved good results [11]. On the application, data analytics companies were developing services that helped companies discover, attract, and assess industry influence by analyzing blogs on the Internet.At present, there are still some issues that need to be addressed when using the in-depth learning model to address short text sensitivity analysis. The most important thing is not to focus effectively on the keywords of emotion in the text and not to rely on linguistic knowledge such as textual information and emotional sources. This unique emotional information must be fully utilized to achieve the best performance of the model. To address the above issues, a short text sensitivity analysis capsule model combining a circulatory neural network and a two-way GRU network is proposed to analyze public perceptions of ideological and political education. The model first focuses on the word vectors in the text in order to focus on emotionally important words in the short text. The advantages of CNN and BI-GRU are combined in the unpacking phase. Finally, according to each category of emotion, vectors are used to express emotional properties, and an emotional capsule is created, which improves the ability to express design features.


## 3. Methods

### 3.1. Fusion Convolutional Neural Network

The textual sensitivity analysis capsule design framework, combined with the circulatory neural network and the bidirectional GRU (MC-BiGRU-Capsule), consists of four parts: attention level, feature breakdown, character integration, and emotional capsule construction.(1)
Layers: these systems contain multiple control systems that capture the ideas expressed in the text, encode the relationships between words, and create clear translations of the text.(2)
Eigen decomposition provides text message vectors based on multiple norms for CNN and Bi-GRU, respectively. The CNN rotates with 3 × 300, 4 × 300, 5 × 300, and 512 broken kernels in step 1, and then focuses on converting the N-gram energy of words into sentences and the structure’s next level of structure. Therefore, the convolution function is only used to obtain the local product of the text. The Bi-GRU model makes the text temporarily pass through the GRU before and after the GRU, removing material from the Earth.(3)
Feature aggregation: feature vector H is decomposed by dialing the input of local features and global semantic properties, and the global averaging layer library vector H takes the representation of the text sample function and calculates the loss function [12].(4)
Create thought capsules: the number of thought capsules corresponding to a set of thoughts. For example, two capsules correspond to positive and negative emotions, and each set of needs is called a “feature capsule.” To get the probability of activating the capsule, enter the function vector H attached to the previous step in the emotional capsulePi is calculated and the feature representation rs,i is reconstructed in combination with the attention mechanism. The probability of activating the capsule is considered active if it is the highest of all the capsules; otherwise, it will be inactive. A characteristic feature of the activation state capsule is the emotional classification of the input text, which is the model output.


### 3.2. Attention Layer

Attention mechanisms can focus on important information in the text. This study uses a variety of methods to obtain important information about sentences from multiple locations.For the textS=w1,w2,…,wL with given length L, where wi is the second word in the sentence, and each word is plotted on a D-dimensional vector, i.e., S∈RL×D.First, convert the word vectorS into a linear form and divide it into three matrices of the same size Q∈RL×D, K∈RL×D,V∈RL×D and mapped to multiple different subspaces, as shown in the following formula:(1)Q1,…,Qh=QWQ1,…,QWQh,K1,…,Kh=KWK1,…,KWKh,V1,…,Vh=VWV1,…,VWVh.In Formula (1), Qi,Ki,Vi is a query, key, and value matrix for each subspace. WQi,WKi,WVi is a conversion matrix, and h is the number of heads.Then calculate the value of attention for each subspace in parallel, as shown in the following equation:(2)headi=softmaxQi,KiTdVi.In Formula (2), headi is the attention value of the i th sub-space, and d is to prevent the gradient from disappearing during the return distribution, the focus matrix is changed to a standard normal distribution.Then, as shown in the following equation, the attention values of each subspace are connected and converted linearly.(3)Multi_head=concathead1,⋯,headhWM.In Formula (3), WM is the transformation matrix, Multi_head The meaning of all the sentences, the meaning of unification, is the act of unification. Finally, connect the residual connection between the Multi_head and S is used to obtain the sentence matrix shown in the following formula:(4)X=residual_ConnectS,Multi_head.In Formula (4), X∈RL×D is the output of multiple attention, residual_Connect is the residual operation.


### 3.3. Fusion of CNN and Bidirectional GRU Text Feature Extraction

To provide a more detailed understanding of text-sensitivity features, this study combines the advantages of rotating neural networks and two-way GRU text characterization to model text sentimental features from the local level to the global level.


### 3.4. Text Feature Extraction Based on CNN

Inspired by visual arts research in bio-agriculture, the joint neural network’s ability to learn and express energy resources has been widely used in the field of natural languages, such as the distribution of text and the distribution of hearing [13]. In the native function of CNN, the word vector formed by a sentence is used as a single input, and then the rotation function is performed by multiple rotation kernels matching the size of the word vector to obtain the attributes of several consecutive words.During the study, a B-turn filter was selected to extract the local characteristics of the multifocus output matrixX, and the feature matrix is Ci=Ci,1,Ci,2,⋯,Ci,B∈RL−k+1×B, where Ci,B=c1,c2,⋯,cL−k+1∈RL−k+1 is the B column vector in Ci. Element cj. This vector can be obtained by the following formula:(5)cj=fW⋅xj:j+k−1+b.In Formula (5), f is the activation function ReLU, W∈Rk×D is the convolution kernel, k is the window width, xj:j+k−1∈Rk×D. K represents the vectors of the word connecting the first and last two, and b is a biased term.


### 3.5. Text Feature Extraction Based on Bidirectional GRU

Machine learning techniques have traditionally been limited to preinformation based on material in semantic models, whereas repetitive neural networks (RNNs) can model all previous information in language [14]. However, the standard RNN has the problem of gradient disappearance or explosion. LSTM networks and GRU networks overcome this problem by selectively influencing the state of each moment in the model through some “gateway” structure. In the LSTM version, the GRU replaces the forget-me-not and entry-level update door in the LSTM. The structural definition of the GRU is shown in Figure 2, and the results of the relevant calculations are shown in formulas (6)–(9).(6)zt=σWzxt+Uzht−1,(7)rt=σWrxt+Urht−1,(8)h˜t=tanhWhxt+Uhht−1⊙rt,(9)ht=1−zt⊙h˜t+zt⊙ht−1.Figure 2
GRU unit structure diagram.In the Formulas,Wz,Wr,Wh,Uz,Ur,Uh are the weight matrix of GRU. σ is the sigmoid function. ⊙ is elements multiplication. zt is an update gate that controls the update level of the GRU unit activation value, which is determined by the current input state and the state of the top hidden layer. rt is a reset gateway that combines the new input information with the original information. ht is the hidden layer. h˜t is the hidden layer of the candidate. To sum up, compared with the LSTM network, the GRU network reduces the design and complexity and reduces the test cost.State changes in classical repetitive neural networks are one way. However, in some cases, the output of the current state is not only related to the previous state but also related to the next state [15]. For example, the prediction of missing words in a sentence must not only determine the preposition but also the meaning of the text that follows, and the emergence of bidirectional recurrent neural networks solves this problem.A bidirectional repetitive neural network connects two unidirectional RNNs. Every time two RNNs enter a state in the same direction at the same time, the output is determined together, making it more accurate. Dual GRUs are created by transforming RNNs in bidirectional neural networks with standard GRUs.The design uses a bidirectional GRU network to study semantic data globally through various agreed-upon matrices. H layers during training. The specific calculation process is shown in formulas (10)–(12).(10)h⟶t=GRUX,h⟶t−1,t∈1,L.(11)h←t=GRUX,h←t+1,t∈L,1,(12)h˜t=tanhWhxt+Uhht−1⊙rt,Ht=h⟶t,h←t.In the formulas,h⟶0 and h←t+1 are both initialized as zero vectors. h⟶t∈RL×d . The vector matrix that combines the previous information is an expression of the sensory properties of the word X. h←t∈RL×d is the expression of the sentiment feature of the word vector matrix X integrating the later information. Moreover, d is the dimension of the GRU unit output vector. Ht∈RL×2d combine the following information. Also, d is the size of the output vector of the GRU unit.


### 3.6. Feature Fusion

Neural network organization can reduce data loss by decomposing the internal components of the text. A bidirectional GRU network temporarily passes all text and provides global semantic storage [16]. This study combined the advantages of a collapsible neural network and a two-way GRU network and used a global average integration method to combine local properties and global semantic properties of text to obtain a typical representation of a text example Vs, this improves the design features.During the experiment, the circulation vector B and the output vector dimension in the convolutional neural network were used2d. It connects the function vectors generated by the two networks by merging and connecting the two-way GRU network with the same meaning and as shown in the following formula:(13)H=concatC,Ht.In Formula (13), H∈Rl+L×2d is the spliced vector, and C=C1,C2,⋯,Cn,C∈Rl×B is the output vector of the convolutional neural network, Ht=h1,h2,⋯,hL,Ht∈RL×2d . The output vector and concat of the two-way GRU is the connecting function.To combine the mean values of the H vector to create the feature points, use the global mean aggregation layer and finally get the feature vectorVs∈R2d. To increase the robustness of the model and to avoid over-tuning, these function points have the characteristic of text-sensitive cases. The results of the calculations are shown in the following formula:(14)Vs=globalaveragepoolingH.In Formula (14), global average pooling is the global average pooling operation.


### 3.7. Sentimental Capsule Construction

Surveillance module: the imaging module combines an H-vector device with a surveillance module to create realistic images in the capsule. The face-lobbing mechanism enables the demo module to measure the importance of words in different texts. For example, “wide” can provide recommendations for hotel reviews, but it does not matter in movie reviews [17]. The formula for calculating the attention mechanism is shown in formulas (15)–(17).(15)ui,t=tanhWwH+bw,(16)αi,t=expui,tTuw∑texpui,tTuw,(17)vc,i=∑tαi,tH.In the formula, H is the representation of the text after the attachment, and H is inserted into the fully connected layer to obtain the hidden representation of ui andt. The similarity between ui, t, and a randomly initiated context vector uw. Calculations are made to determine the significance of the words, and the weight of the words in the sentence is normalized using the softmax function to obtain ai, t. Weigh the vector H along the weight matrix to obtain the sum vc,i∈R2d of the attention mechanism. Ww and uw are weight matrices, and bw is the bias value learned during training [18]. The attention-grabbing mechanism creates deeper properties at the top level of vc, i, and captures key information in semantic sensitivity.The probability module calculates the probability of activating the capsule according to the Vc.i semantic properties combined with the following formula:(18)Pi=σWP,ivc,i+bp,i.In Formula (18), Pi is the probability of activating the first capsule, WP,i and bp,i are the weight matrix and the bias matrix, and σ is a function of activating the sigmoid gland.The recovery module multiplies the Vc.i semantic properties by a probability matrixPi obtain a description of the reconstructed semantic properties rs,i∈R2d, as shown in the following formula:(19)rs,i=Pivc,i.The three modules in the capsule complement each other. Each capsule has a characteristic (mental category) for entering text. Therefore, it is likely to be activated if the text sensitivity matches the capsule propertiesPi of the capsule should be maximum, and the reconstruction feature rs,i of the capsule output should be most similar to the text instance feature Vs.Therefore, the spinal loss function is confirmed as shown in (20) and (21).(20)Jθ=∑max0,1+∑i=1NyiPi,(21)Uθ=∑max0,1+∑i=1NyiVsrs,i.In Formula (20) and (21), yi is the sensitivity class label corresponding to the text. The final loss function is the sum of (20) and (21).(22)Lθ=Jθ+Uθ.


## 3.1. Fusion Convolutional Neural Network

The textual sensitivity analysis capsule design framework, combined with the circulatory neural network and the bidirectional GRU (MC-BiGRU-Capsule), consists of four parts: attention level, feature breakdown, character integration, and emotional capsule construction.(1)
Layers: these systems contain multiple control systems that capture the ideas expressed in the text, encode the relationships between words, and create clear translations of the text.(2)
Eigen decomposition provides text message vectors based on multiple norms for CNN and Bi-GRU, respectively. The CNN rotates with 3 × 300, 4 × 300, 5 × 300, and 512 broken kernels in step 1, and then focuses on converting the N-gram energy of words into sentences and the structure’s next level of structure. Therefore, the convolution function is only used to obtain the local product of the text. The Bi-GRU model makes the text temporarily pass through the GRU before and after the GRU, removing material from the Earth.(3)
Feature aggregation: feature vector H is decomposed by dialing the input of local features and global semantic properties, and the global averaging layer library vector H takes the representation of the text sample function and calculates the loss function [12].(4)
Create thought capsules: the number of thought capsules corresponding to a set of thoughts. For example, two capsules correspond to positive and negative emotions, and each set of needs is called a “feature capsule.” To get the probability of activating the capsule, enter the function vector H attached to the previous step in the emotional capsulePi is calculated and the feature representation rs,i is reconstructed in combination with the attention mechanism. The probability of activating the capsule is considered active if it is the highest of all the capsules; otherwise, it will be inactive. A characteristic feature of the activation state capsule is the emotional classification of the input text, which is the model output.


## 3.2. Attention Layer

Attention mechanisms can focus on important information in the text. This study uses a variety of methods to obtain important information about sentences from multiple locations.For the textS=w1,w2,…,wL with given length L, where wi is the second word in the sentence, and each word is plotted on a D-dimensional vector, i.e., S∈RL×D.First, convert the word vectorS into a linear form and divide it into three matrices of the same size Q∈RL×D, K∈RL×D,V∈RL×D and mapped to multiple different subspaces, as shown in the following formula:(1)Q1,…,Qh=QWQ1,…,QWQh,K1,…,Kh=KWK1,…,KWKh,V1,…,Vh=VWV1,…,VWVh.In Formula (1), Qi,Ki,Vi is a query, key, and value matrix for each subspace. WQi,WKi,WVi is a conversion matrix, and h is the number of heads.Then calculate the value of attention for each subspace in parallel, as shown in the following equation:(2)headi=softmaxQi,KiTdVi.In Formula (2), headi is the attention value of the i th sub-space, and d is to prevent the gradient from disappearing during the return distribution, the focus matrix is changed to a standard normal distribution.Then, as shown in the following equation, the attention values of each subspace are connected and converted linearly.(3)Multi_head=concathead1,⋯,headhWM.In Formula (3), WM is the transformation matrix, Multi_head The meaning of all the sentences, the meaning of unification, is the act of unification. Finally, connect the residual connection between the Multi_head and S is used to obtain the sentence matrix shown in the following formula:(4)X=residual_ConnectS,Multi_head.In Formula (4), X∈RL×D is the output of multiple attention, residual_Connect is the residual operation.


## 3.3. Fusion of CNN and Bidirectional GRU Text Feature Extraction

To provide a more detailed understanding of text-sensitivity features, this study combines the advantages of rotating neural networks and two-way GRU text characterization to model text sentimental features from the local level to the global level.


## 3.4. Text Feature Extraction Based on CNN

Inspired by visual arts research in bio-agriculture, the joint neural network’s ability to learn and express energy resources has been widely used in the field of natural languages, such as the distribution of text and the distribution of hearing [13]. In the native function of CNN, the word vector formed by a sentence is used as a single input, and then the rotation function is performed by multiple rotation kernels matching the size of the word vector to obtain the attributes of several consecutive words.During the study, a B-turn filter was selected to extract the local characteristics of the multifocus output matrixX, and the feature matrix is Ci=Ci,1,Ci,2,⋯,Ci,B∈RL−k+1×B, where Ci,B=c1,c2,⋯,cL−k+1∈RL−k+1 is the B column vector in Ci. Element cj. This vector can be obtained by the following formula:(5)cj=fW⋅xj:j+k−1+b.In Formula (5), f is the activation function ReLU, W∈Rk×D is the convolution kernel, k is the window width, xj:j+k−1∈Rk×D. K represents the vectors of the word connecting the first and last two, and b is a biased term.


## 3.5. Text Feature Extraction Based on Bidirectional GRU

Machine learning techniques have traditionally been limited to preinformation based on material in semantic models, whereas repetitive neural networks (RNNs) can model all previous information in language [14]. However, the standard RNN has the problem of gradient disappearance or explosion. LSTM networks and GRU networks overcome this problem by selectively influencing the state of each moment in the model through some “gateway” structure. In the LSTM version, the GRU replaces the forget-me-not and entry-level update door in the LSTM. The structural definition of the GRU is shown in Figure 2, and the results of the relevant calculations are shown in formulas (6)–(9).(6)zt=σWzxt+Uzht−1,(7)rt=σWrxt+Urht−1,(8)h˜t=tanhWhxt+Uhht−1⊙rt,(9)ht=1−zt⊙h˜t+zt⊙ht−1.Figure 2
GRU unit structure diagram.In the Formulas,Wz,Wr,Wh,Uz,Ur,Uh are the weight matrix of GRU. σ is the sigmoid function. ⊙ is elements multiplication. zt is an update gate that controls the update level of the GRU unit activation value, which is determined by the current input state and the state of the top hidden layer. rt is a reset gateway that combines the new input information with the original information. ht is the hidden layer. h˜t is the hidden layer of the candidate. To sum up, compared with the LSTM network, the GRU network reduces the design and complexity and reduces the test cost.State changes in classical repetitive neural networks are one way. However, in some cases, the output of the current state is not only related to the previous state but also related to the next state [15]. For example, the prediction of missing words in a sentence must not only determine the preposition but also the meaning of the text that follows, and the emergence of bidirectional recurrent neural networks solves this problem.A bidirectional repetitive neural network connects two unidirectional RNNs. Every time two RNNs enter a state in the same direction at the same time, the output is determined together, making it more accurate. Dual GRUs are created by transforming RNNs in bidirectional neural networks with standard GRUs.The design uses a bidirectional GRU network to study semantic data globally through various agreed-upon matrices. H layers during training. The specific calculation process is shown in formulas (10)–(12).(10)h⟶t=GRUX,h⟶t−1,t∈1,L.(11)h←t=GRUX,h←t+1,t∈L,1,(12)h˜t=tanhWhxt+Uhht−1⊙rt,Ht=h⟶t,h←t.In the formulas,h⟶0 and h←t+1 are both initialized as zero vectors. h⟶t∈RL×d . The vector matrix that combines the previous information is an expression of the sensory properties of the word X. h←t∈RL×d is the expression of the sentiment feature of the word vector matrix X integrating the later information. Moreover, d is the dimension of the GRU unit output vector. Ht∈RL×2d combine the following information. Also, d is the size of the output vector of the GRU unit.


## 3.6. Feature Fusion

Neural network organization can reduce data loss by decomposing the internal components of the text. A bidirectional GRU network temporarily passes all text and provides global semantic storage [16]. This study combined the advantages of a collapsible neural network and a two-way GRU network and used a global average integration method to combine local properties and global semantic properties of text to obtain a typical representation of a text example Vs, this improves the design features.During the experiment, the circulation vector B and the output vector dimension in the convolutional neural network were used2d. It connects the function vectors generated by the two networks by merging and connecting the two-way GRU network with the same meaning and as shown in the following formula:(13)H=concatC,Ht.In Formula (13), H∈Rl+L×2d is the spliced vector, and C=C1,C2,⋯,Cn,C∈Rl×B is the output vector of the convolutional neural network, Ht=h1,h2,⋯,hL,Ht∈RL×2d . The output vector and concat of the two-way GRU is the connecting function.To combine the mean values of the H vector to create the feature points, use the global mean aggregation layer and finally get the feature vectorVs∈R2d. To increase the robustness of the model and to avoid over-tuning, these function points have the characteristic of text-sensitive cases. The results of the calculations are shown in the following formula:(14)Vs=globalaveragepoolingH.In Formula (14), global average pooling is the global average pooling operation.


## 3.7. Sentimental Capsule Construction

Surveillance module: the imaging module combines an H-vector device with a surveillance module to create realistic images in the capsule. The face-lobbing mechanism enables the demo module to measure the importance of words in different texts. For example, “wide” can provide recommendations for hotel reviews, but it does not matter in movie reviews [17]. The formula for calculating the attention mechanism is shown in formulas (15)–(17).(15)ui,t=tanhWwH+bw,(16)αi,t=expui,tTuw∑texpui,tTuw,(17)vc,i=∑tαi,tH.In the formula, H is the representation of the text after the attachment, and H is inserted into the fully connected layer to obtain the hidden representation of ui andt. The similarity between ui, t, and a randomly initiated context vector uw. Calculations are made to determine the significance of the words, and the weight of the words in the sentence is normalized using the softmax function to obtain ai, t. Weigh the vector H along the weight matrix to obtain the sum vc,i∈R2d of the attention mechanism. Ww and uw are weight matrices, and bw is the bias value learned during training [18]. The attention-grabbing mechanism creates deeper properties at the top level of vc, i, and captures key information in semantic sensitivity.The probability module calculates the probability of activating the capsule according to the Vc.i semantic properties combined with the following formula:(18)Pi=σWP,ivc,i+bp,i.In Formula (18), Pi is the probability of activating the first capsule, WP,i and bp,i are the weight matrix and the bias matrix, and σ is a function of activating the sigmoid gland.The recovery module multiplies the Vc.i semantic properties by a probability matrixPi obtain a description of the reconstructed semantic properties rs,i∈R2d, as shown in the following formula:(19)rs,i=Pivc,i.The three modules in the capsule complement each other. Each capsule has a characteristic (mental category) for entering text. Therefore, it is likely to be activated if the text sensitivity matches the capsule propertiesPi of the capsule should be maximum, and the reconstruction feature rs,i of the capsule output should be most similar to the text instance feature Vs.Therefore, the spinal loss function is confirmed as shown in (20) and (21).(20)Jθ=∑max0,1+∑i=1NyiPi,(21)Uθ=∑max0,1+∑i=1NyiVsrs,i.In Formula (20) and (21), yi is the sensitivity class label corresponding to the text. The final loss function is the sum of (20) and (21).(22)Lθ=Jθ+Uθ.


## 4. Results and Analysis

The experiments were performed on three English data sets: the Mr. (film review) data package, the IMDB data package, and the SST-5 data package, and the Chinese data package is an ideological and political education review package. The above data sets are widely used for sensitivity classification tasks, which makes the experimental results have a good evaluation effect. Mr.’s dataset is a collection of UK movie reviews. Each sentence is classified as positive or negative, with 5,331 positives and 5,331 negatives. The IMDB file contains 50,000 files from U.S. movie studios that are categorized into positive and negative categories in critical thinking analysis [19]. The SST-5 packet is a continuation of Mr. package files and provides separate training packages, available packages, and test packages, totaling 11,855 sentences. Text can be divided into five categories: “Excellent,” “Good,” “Neutral,” “Poor,” and “Poor”. In this study, SST was trained at the sentence level. After distributing the data for the first time, 3,000 positive feedback and 3,000 negative feedback were obtained from this experiment. An overview of each data set is shown in Table 1.Table 1
Experimental data set statistics.
Data setThe training setValidation setThe test setNumber of categoriesMR8.64k0.97k1.15k4IMDB25.11k50.14k25.21k1SST-5(Sentence-level)8.54k1.07k2.24k5Tan Songbo hotel review3.64k1.29k1.24k2The experiments in this study are based on PyTorch. English uses 300-dimensional glove word vectors to input words. For words not in the dictionary, start randomly using a similar distribution with a value of 0.05. To first train the Chinese word vectors, we use the fastHan tool to label the text, then train the SKPP-gram standard to use big data from Chinese Wikipedia, and resize the Chinese word vectors to 300 dimensions [20]. Headphones use 8 headphones (h = 8) and use the Adam optimizer during modeling with a learning rate of 0.001. Accuracy measures are used to measure the model, and specific areas of the hyper model are not included in Table 2.Table 2
Experimental hyperparameter settings.
ParameterMRIMDBSST-5(Sentence-level)Tan Songbo hotel reviewConvolution kernel width2,1,43,2,51,42,3,5Convolution kernel number513259246289Number of hidden units257126124147Batch size59386131Dropout0.540.410.520.29In this study, 4 common data sets were compared with the 11 models mentioned above. The proposed MC-BiGRU-capsule model provides better class performance than the basic models on the four data sets. The model has an accuracy of 85.3% for the Mr data package, 50.0% for the SST-5 data package, 91.5% for the IMDB data package, and 91.8% for the Chinese data package [21, 22]. The accuracy of the optimal classification model in the 4 data sets is 1.5%, 0.5%, 2.2%, and 1.2% higher than the control tests, respectively.First, for the traditional machine learning process, other groups do better than Mr. IMDB and NBSVM in terms of theoretical and cultural data analysis, which indicates that the neural network model has a better effect on sentiment classification tasks than the traditional method. At the same time, the performance of the capsule model is higher than that of deeply researched models such as CNN, BI-LSTM, and MC–CNN–LSTM, indicating that capsules are used to represent text sentiment features in the sentiment classification task, retaining more sentiment information and improving the model classification performance. In addition, the capsule method has a competitive advantage over model language integration experiments [23].Second, among the in-depth learning methods, MC–CNN–LSTM is better than CNN and BI-LSTM in terms of test performance on all data sets, which verifies the necessity of integrating convolutional neural network local feature extraction and BI-GRU to capture the global text information. Han’s public data shows that compared with MC–CNN–LSTM, our standard planning level is improved by 5.1%, 2.8%, 2.8%, and 1.6%, respectively, indicating that the vector neurons used by the capsule model have higher energy model thinking ability. In-depth studies, including an understanding of Mr.'s language and thinking skills and SST-5 show better classes compared to other base models. However, the MC-BiGRU-capsule model proposed in this study achieves 3.2%, 2.4%, and 3.4% higher accuracy than NSCL, NSCL, and multi-Bi-LSTM models, respectively and shows performance in multiple categories of class records [24]. Furthermore, the LR-Bi-LSTM and NSCL models rely heavily on linguistic knowledge such as sensory vocabulary and energy regulators. It is worth noting that constructing such language knowledge requires a great deal of human intervention. The multi-Bi-LSTM model is more detailed than the above two models but is also based on in-depth knowledge and concepts, which is very labor- and time-consuming. However, the research model does not require any linguistic or mental knowledge in the model, the capsule model of the textual mental model achieves more results than the sample instructions, and the depth of communication knowledge and information needs is good and easy [25].This is because IMDB file sets are long file sets and Mr. file sets are short file sets. RNN-capsule uses a repetitive neural network to generate ad hoc text, average latent features along sentence length, and obtain behavioral representations of the final example. The longer the sentence, the fewer vector representations. It also does not better represent the categories of sensitive text that affect the final structure. Therefore, RNN capsules do not work properly in the IMDB file configuration. Capsule Network Capsule A and Capsule B use a dynamic routing mechanism, connecting to fully connected capsule layers to replace and sort-merge layers to form capsules. The length of the text has little effect on the capsule network. The classification accuracy of the MC-BiGRU-capsule model proposed in this study is higher than that of the RNN-capsule of the four datasets, and the classification accuracy of the MC-BiGRU-capsule model of the IMDB dataset is higher than that of the network capsule a and capsuleb. The results show that the advantages of multi-listen-encoded word relations, integrated split neural network, and BI-GRU decompression function RNN-capsule overcomes the limitations of scripted long vector representation and global media sharing layer representation to create Chinese text and English. Data package example features also demonstrate the strength and overall ability of the mc-BiGRU-capsule.In this study, we introduce the concept of capsules into the model and use vector neurons to transform scalar neurons. This not only reduces data loss but also improves the ability to model emotions. Also, vector-based training differs from neural network architectures gentleman, a working model aimed at understanding how vector training affects a set of files. By changing the size of the Chinese sample vector and the size of the inverse vector of the sample capsule, the variation of the instrument sample size accuracy can be obtained. Experimental results show that the distribution model is more accurate when large vectors are used to represent the sensitivity of text. In this way, when the learning target is a vector, the ability of the text to express the target is improved, and the text can represent various objects. The results are shown in Figure3.Figure 3
Variation of accuracy with vector dimensions.To clarify that multiple listeners can capture thought words in texts and encode word relationships, this study shows the weights of words in sentences and reveals the meanings of words and important features of texts. Take the positive and negative patterns of the IMDB dataset as an example, which reminds us of the delicate material of the text.Dynamic word vectors BERT works well in many languages. Compared to static languages such as Glove and Word2Vec, BERT can distinguish deep meanings from text and overcome racism by combining two encodings and different meanings to obtain word meanings. This study uses BERT dynamic word vectors from the IMDB database. Furthermore, the BERT was combined with the proposed MC-BiGRU-capsule model and compared with the precision-adjusted SentiBERT model, a pre-prepared sensitivity dictionary from BERT.Since the BERT language is large and repeatable, many scholars use preplanned BERT models to refine the following activities. However, due to the limited space of the entries, many designs will cause problems such as quality and time. As shown in Table3, the MC-BiGRU-capsule model in this study is not only designed for GloVe static word vectors but also has better class performance than the BERT model and ULMFIT (an LSTM-based preliminary scheme). The accuracy of the combined distribution of word dynamic vectors is improved by 1.2%, which is a 0.8% improvement over the SentiBERT model. Based on these models, Bert introduces a good vector design that enhances their performance and enhances the advantages of the mc-BiGRU model-capsule model.Table 3
Comparison of experimental results combined with a dynamic word vector (unit: %).
ModelAccuracyBERT90.14ULMFIT91.21SentiBERT90.94BERT + MC-BiGRU-capsule91.89MC-BiGRU-capsule91.55


## 5. Conclusions

This study proposes a capsule model of sensitive literature connecting neural networks and two GRU methods. The model focuses on capturing words in the text and encoding word relationships; solving the problem that capsule networks cannot select; and focusing on keywords in text distribution. To eliminate multilevel, large-scale text-sensitive objects, CNN writes local features and uses a bidirectional GRU network to address global devices. The use of vector neurons (capsules) instead of scalar neurons to model text sensitivity is more classified as a method of integrating language knowledge and emotional resources, proving its ability to express capsule design features. Experiments on various data sets confirm the effectiveness of the model.The next step may be to improve the internal mechanisms of the emotional capsule, such as optimizing the attention-grabbing mechanism. At the same time, the vector must better express the emotional properties and improve the ability to integrate functions to increase the stability and efficiency of the model.



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