# Analysis and Evaluation of the Impact of Integrating Mental Health Education into the Teaching of University Civics Courses in the Context of Artificial Intelligence

Authors: JingJing Wu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In higher education teaching work, college students not only need to master the professional knowledge and professional skills they learn during their school study but also need to improve their self-education and self-cultivation and constantly improve their comprehensive ability of learning. At present, there are differences and relationships between the education of college students in civic and mental normal education, and how to play the role of the integration of the two educations has become a problem that needs to be considered in the current work of college students’ training. The integration between civic education and mental normal education can make up for the shortcomings of monolithic civic education and mental normal education work and also optimize the teaching methods between them from a certain perspective to achieve the development goals of complementing each other and not being independent of each other, so that students can understand more learning methods and contents that promote the normal development of their own minds and minds. In response to the problem that mind-normal education cannot be automatically integrated into the teaching of university thought and political science courses, in the context of artificial intelligence, this paper proposes a multi-channel-based mind-normal and ideological and political information fusion model. The model has two channels, BERT+CNN and BERT and BiLSTM-Attention; firstly, the pretraining model BERT is used to obtain the word vector representation of the fused text context; then, the CNN network of channel one is used to enhance the ability of local feature extraction of the text, and the BiLSTM-Attention model of channel two enhances the ability of long sequence text processing and is key. Finally, the fused features of channel 1 and channel 2 are classified using a softmax excitation function. To verify the effectiveness of the proposed model, experiments are conducted on public datasets to demonstrate the effectiveness of the proposed method.


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## Body

## 1. Introduction

Curriculum socialism mainly refers to the effective integration of socialist education into all the courses of colleges and universities. This advocates the process required to understand complete courses, the process involved in all-round and all-staff education. It also acts as an effective way for colleges and universities to reflect on the ideological attributes of education and explore the moral education function of courses in this new era. It is also an important part of the talent cultivation system of colleges and universities, as well as an important part of socialist work. After the concept of curriculum socialism is put forward, the teaching activities of the course of normal mental education in colleges and universities should not be limited to normal mental education but also focus on the practice of curriculum socialism [1, 2].Teachers of the course of normal mental education in colleges and universities must fully recognize the value of curriculum socialism in the teaching process, timely excavate its value connotation, and at the same time pay attention to the integration [3–6], in the classroom to infiltrate the curriculum socialism in all aspects of normal mental education in a silent way, so as to effectively play the role of the two educational synergy, and truly cultivate for society to support the party and the socialist system. In this way, we can effectively bring into play the synergy between the two education programs, truly cultivate builders and successors who support the party and the socialist system, and effectively improve the quality of personnel training. In order to realize the curriculum socialism in the course of normal education, the teachers of normal education must pay attention to the realization of the function of educating people. Nowadays, the environment at home and abroad is becoming more and more complicated, and the psychological problems of contemporary college students are becoming more and more prominent in this environment, and the number of crisis events has increased significantly, and some students even have a lack of ideals and beliefs, which makes it more difficult for educators. In this situation, the purely normal education of the mind naturally cannot effectively respond to the development of the new era; teachers in the construction of the normal education curriculum should not only solve the psychological problems of students in a timely manner but also need to pay attention to the effective guidance of Marxist values and methodology. In addition, we need to focus on the effective guidance of Marxist values and methodology, to strengthen students’ ideals and beliefs in the course of normal mental education, and to use curriculum socialism to further improve students’ moral level and ideology, so as to truly promote students’ overall development and improvement [5–7].Curriculum socialism promotes the normal education of the mind, promotes the realization of the goal of moral education, and effectively responds to the pluralism of college students’ thoughts through mainstream values. Under the background of value diversification, college students have flexible and avant-garde thinking and diversified value orientation, and pragmatism, utilitarianism, and egoism are also the value ideas nowadays [8]. For this reason, teachers of the course of normal mental education in colleges and universities can effectively respond to the diversity of college students’ thoughts with the help of mainstream values during the course practice, so as to truly optimize the teaching of normal mental education and promote the effective penetration and realization of socialism in the course. To focus on the effective integration of socialist education and normal education, teachers need to pay attention to the effective integration between socialist education and normal education to realize socialism in the teaching of normal education courses in colleges and universities, so as to effectively promote the overall improvement of educational objectives. Nowadays, some students in colleges and universities are not mentally normal and lack firm ideal beliefs [9–11]. Although the course of mentally normal education can improve students’ stress coping ability, emotion management ability, and human interaction ability and promote students’ healthy growth, the effect of talent cultivation still cannot meet the requirements of the new era compared with the goal of cultivating people with moral character in the new era. However, compared with the goal of cultivating people with moral values in the new era, the effect of talent cultivation still cannot meet the requirements of the new era. The diagram of the integration of mental health and civic education is shown in Figure 1. The unique contribution of the work includes the following:
(i)
A model was developed, which uses two channels, namely, the BERT+CNN and BERT+BiLSTM(ii)
A pretraining model BERT is developed to obtain the word vector representation of the fused text context(iii)
The CNN network of channel one is implemented to enhance the ability of local feature extraction of the text(iv)
The BiLSTM-Attention model of channel two is used to enhance the ability of long sequence text processing(v)
The fused features of channel 1 and channel 2 are classified using a softmax excitation function(vi)
The effectiveness of the model is evaluated against the traditional state-of-the-art approachesFigure 1
Mental health and civic education integration diagram.The organization of the paper is as follows: Section2 reviews various studies relevant to mental health condition and course integration. Section 3 explains the methodology involved in the proposed study in detail. Section 4 presents the description of the dataset, experimental analysis, and results. Finally, Section 5 provides the conclusion of the work done.


## 2. Related Work

### 2.1. Mental Health and Socialism

Teachers in colleges and universities are the key to the dissemination of new ideas and the cultivation of high-quality talents [12–14]. To effectively realize socialism in the teaching of mental normal education courses in colleges and universities, we must first build a team of mental normal education teachers with strong business ability, high level of education, and excellent political quality, so as to provide a good guarantee for the implementation of socialism education. In this process, teachers of normal mental education should not only have the professional ability and knowledge of the curriculum but also have the awareness and ability to carry out socialist construction and be able to systematically educate students about socialism with Chinese characteristics and the Chinese dream, as well as educate students about core socialist values and excellent Chinese traditional culture, so as to truly strengthen students’ ideal beliefs and effectively play the role of normal mental education. The course has the function of educating people. First, teachers of mental normal education in colleges and universities must constantly improve their own political cultivation, firm their own Marxist beliefs, constantly study and improve their own political literacy in the course of mental normal education, and really use the course of mental normal education to transmit socialist values to students, so as to improve the teaching infectivity. Secondly, teachers also need to master the laws, characteristics, and discourse of this education, so that they can really internalize the core of socialist education effectively in the practice of mindfulness education courses [15, 16].To optimize the teaching contents and explore the socialist elements in the course of normal mental education, the socialist elements are the core and key to the effective construction of socialism in the course. Although it can effectively explain some psychological phenomena and laws and convey relevant psychological knowledge to students, its realization angle is obviously insufficient when analyzed from the perspective of students’ cultivation goals. For this reason, teachers must actively explore and study China’s traditional culture in the course of curriculum practice, introduce the essence of China’s culture into the normal mental education curriculum practice in time, consciously organize the big socialist elements in the curriculum content effectively, and then tell students the content in line with the core socialist values and the typical representative deeds of people in the classroom, so that students can receive socialist education in the normal mental education curriculum. In this way, the implicit socialist education concept can be effectively penetrated into the course of normal mental education in colleges and universities, so as to improve the effect of normal mental education. For example, when teachers teach students about psychological qualities of personality, they can use some patriotic heroes, intellectuals, and model workers who emerged in the process of China’s development as cases and educate students with their optimism, perseverance, and dedication, so as to effectively promote the realization of socialism in the course of mental normal education in colleges and universities [17, 18]. The various strategies used in mental health education include aggression management, depression management, emotional regulation, self-advocacy schools, time management skills, and various others. In this way, we can effectively promote the realization of socialism in the curriculum of normal mental education in colleges and universities. The schematic diagram of the fusion method is shown in Figure 2.Figure 2
Diagram of the integration mode.


### 2.2. Course Integration

During the period of getting along with students in classroom teaching, it is easy to have frictional problems in mind and psychology [19, 20]. University is an important stage of students’ learning and life development, and it is very necessary to strengthen socialism and mind-normal education. In the past, teaching work was usually done in the traditional sense, in the form of popularizing solid theoretical knowledge and basic content to students, which in turn also made students’ learning too superficial. Also in socialist education work, the obvious role and value of combining the two in education are not played by bringing in the knowledge of mind-normal education and other forms. In the integration of the two educational contents and modes, teachers must bring the knowledge of normal-minded education into the classroom in socialist education and also enable students to have a deeper knowledge of socialism and normal mindedness by solving relevant problems and teaching practical problems, so as to achieve the ultimate goal of human education. In the process of bringing in mindfulness education, teaching situations can be created for students, and knowledge about mindfulness education can be shared with students through teaching videos and teaching materials, so as to create an atmosphere for the development of mindfulness education infiltrated in socialist education, so that students can understand mindfulness education knowledge from the side and clarify the commonality between the two education in terms of content and methods. In addition, in the integration of various educational contents, it is also necessary to take the students’ own learning characteristics and behaviors as the starting point and to track the students’ ideological behavior and learning behavior in learning and life. And through practical solutions to students’ ideological and psychological problems and other forms, the goal of interactive education and integration of the two is achieved.Mutual education teams of the two were reasonably allocated. Based on the teaching of talent cultivation in colleges and universities, it is necessary to reasonably allocate the teams of socialism and mind-normal education and also to provide guarantee for the integration and development work of the two education modes through the forms of mutual cooperation and mutual development between education teams. In the education and teaching, socialist and mentally normal education are teams with differences, in order to achieve the purpose of effective cooperation and concordant development between the two teams. It is also necessary to increase the cooperation and training of the two teams, so that the mentally normal teachers and socialist health teachers can raise their attention to the work of integrated education and use forms such as joint analysis of students’ mentally normal problems and socialist learning problems to promote the synergistic development of psychological counseling training and socialist education work. Schools need to increase the training of their teaching teams, and this training is a sure way for them to improve the quality of their teaching. In the process of training, it can enrich socialist teachers’ concept and connotation of mind-normal education and broaden mind-normal education teachers’ knowledge of socialist education and educational skills. It can also adopt the way of combining socialist and mind-normal education with other subjects to achieve the goal of diversified development and mutual development. In the reasonable allocation of the two mutual education teams, it is also necessary to break through the traditional education concept, adopt a step-by-step education model, appropriately strengthen the interaction and communication between the two kinds of education knowledge, and the education and teaching teams of the two can also enhance students’ knowledge of integrated education through regular lectures, class meetings, etc., so as to continuously bring the educational role of the two mutual education and integrated education teams into play [21–23].Adopt complementary education and teaching methods. At present, in the training of college students, the integration methods of socialism and mind-normal education contain many; in order to bring out the basic education ways and advantages, it is also necessary to adopt the education and teaching methods with complementary advantages to achieve the purpose of gradual integration and development of the two. In order to strengthen the psychological and ideological qualities of students, it is necessary to provide effective help for their healthy development through complementary teaching resources and teaching methods. Among the complementary educational and teaching methods, socialist education can be used to develop classroom teaching videos to explain the theory of socialist education to students, and in the development of teaching videos, cases of mind-normal education can also be incorporated, so that students can analyze the confusion and ideological and psychological problems encountered in their studies through the perspective of mind and psychology. School teachers can build a network platform for socialism teaching and psychological counseling, so that students can use the network platform to interact and communicate to solve their own ideological and psychological problems in a timely manner, and in their daily teaching, they can also carry out educational activities that complement the advantages of the two, so that students can learn about socialism and normal mind knowledge in the activities, so that students can get more opportunities to develop themselves and improve their own abilities. This will allow for a better identification with the work of integrating education of both.


## 2.1. Mental Health and Socialism

Teachers in colleges and universities are the key to the dissemination of new ideas and the cultivation of high-quality talents [12–14]. To effectively realize socialism in the teaching of mental normal education courses in colleges and universities, we must first build a team of mental normal education teachers with strong business ability, high level of education, and excellent political quality, so as to provide a good guarantee for the implementation of socialism education. In this process, teachers of normal mental education should not only have the professional ability and knowledge of the curriculum but also have the awareness and ability to carry out socialist construction and be able to systematically educate students about socialism with Chinese characteristics and the Chinese dream, as well as educate students about core socialist values and excellent Chinese traditional culture, so as to truly strengthen students’ ideal beliefs and effectively play the role of normal mental education. The course has the function of educating people. First, teachers of mental normal education in colleges and universities must constantly improve their own political cultivation, firm their own Marxist beliefs, constantly study and improve their own political literacy in the course of mental normal education, and really use the course of mental normal education to transmit socialist values to students, so as to improve the teaching infectivity. Secondly, teachers also need to master the laws, characteristics, and discourse of this education, so that they can really internalize the core of socialist education effectively in the practice of mindfulness education courses [15, 16].To optimize the teaching contents and explore the socialist elements in the course of normal mental education, the socialist elements are the core and key to the effective construction of socialism in the course. Although it can effectively explain some psychological phenomena and laws and convey relevant psychological knowledge to students, its realization angle is obviously insufficient when analyzed from the perspective of students’ cultivation goals. For this reason, teachers must actively explore and study China’s traditional culture in the course of curriculum practice, introduce the essence of China’s culture into the normal mental education curriculum practice in time, consciously organize the big socialist elements in the curriculum content effectively, and then tell students the content in line with the core socialist values and the typical representative deeds of people in the classroom, so that students can receive socialist education in the normal mental education curriculum. In this way, the implicit socialist education concept can be effectively penetrated into the course of normal mental education in colleges and universities, so as to improve the effect of normal mental education. For example, when teachers teach students about psychological qualities of personality, they can use some patriotic heroes, intellectuals, and model workers who emerged in the process of China’s development as cases and educate students with their optimism, perseverance, and dedication, so as to effectively promote the realization of socialism in the course of mental normal education in colleges and universities [17, 18]. The various strategies used in mental health education include aggression management, depression management, emotional regulation, self-advocacy schools, time management skills, and various others. In this way, we can effectively promote the realization of socialism in the curriculum of normal mental education in colleges and universities. The schematic diagram of the fusion method is shown in Figure 2.Figure 2
Diagram of the integration mode.


## 2.2. Course Integration

During the period of getting along with students in classroom teaching, it is easy to have frictional problems in mind and psychology [19, 20]. University is an important stage of students’ learning and life development, and it is very necessary to strengthen socialism and mind-normal education. In the past, teaching work was usually done in the traditional sense, in the form of popularizing solid theoretical knowledge and basic content to students, which in turn also made students’ learning too superficial. Also in socialist education work, the obvious role and value of combining the two in education are not played by bringing in the knowledge of mind-normal education and other forms. In the integration of the two educational contents and modes, teachers must bring the knowledge of normal-minded education into the classroom in socialist education and also enable students to have a deeper knowledge of socialism and normal mindedness by solving relevant problems and teaching practical problems, so as to achieve the ultimate goal of human education. In the process of bringing in mindfulness education, teaching situations can be created for students, and knowledge about mindfulness education can be shared with students through teaching videos and teaching materials, so as to create an atmosphere for the development of mindfulness education infiltrated in socialist education, so that students can understand mindfulness education knowledge from the side and clarify the commonality between the two education in terms of content and methods. In addition, in the integration of various educational contents, it is also necessary to take the students’ own learning characteristics and behaviors as the starting point and to track the students’ ideological behavior and learning behavior in learning and life. And through practical solutions to students’ ideological and psychological problems and other forms, the goal of interactive education and integration of the two is achieved.Mutual education teams of the two were reasonably allocated. Based on the teaching of talent cultivation in colleges and universities, it is necessary to reasonably allocate the teams of socialism and mind-normal education and also to provide guarantee for the integration and development work of the two education modes through the forms of mutual cooperation and mutual development between education teams. In the education and teaching, socialist and mentally normal education are teams with differences, in order to achieve the purpose of effective cooperation and concordant development between the two teams. It is also necessary to increase the cooperation and training of the two teams, so that the mentally normal teachers and socialist health teachers can raise their attention to the work of integrated education and use forms such as joint analysis of students’ mentally normal problems and socialist learning problems to promote the synergistic development of psychological counseling training and socialist education work. Schools need to increase the training of their teaching teams, and this training is a sure way for them to improve the quality of their teaching. In the process of training, it can enrich socialist teachers’ concept and connotation of mind-normal education and broaden mind-normal education teachers’ knowledge of socialist education and educational skills. It can also adopt the way of combining socialist and mind-normal education with other subjects to achieve the goal of diversified development and mutual development. In the reasonable allocation of the two mutual education teams, it is also necessary to break through the traditional education concept, adopt a step-by-step education model, appropriately strengthen the interaction and communication between the two kinds of education knowledge, and the education and teaching teams of the two can also enhance students’ knowledge of integrated education through regular lectures, class meetings, etc., so as to continuously bring the educational role of the two mutual education and integrated education teams into play [21–23].Adopt complementary education and teaching methods. At present, in the training of college students, the integration methods of socialism and mind-normal education contain many; in order to bring out the basic education ways and advantages, it is also necessary to adopt the education and teaching methods with complementary advantages to achieve the purpose of gradual integration and development of the two. In order to strengthen the psychological and ideological qualities of students, it is necessary to provide effective help for their healthy development through complementary teaching resources and teaching methods. Among the complementary educational and teaching methods, socialist education can be used to develop classroom teaching videos to explain the theory of socialist education to students, and in the development of teaching videos, cases of mind-normal education can also be incorporated, so that students can analyze the confusion and ideological and psychological problems encountered in their studies through the perspective of mind and psychology. School teachers can build a network platform for socialism teaching and psychological counseling, so that students can use the network platform to interact and communicate to solve their own ideological and psychological problems in a timely manner, and in their daily teaching, they can also carry out educational activities that complement the advantages of the two, so that students can learn about socialism and normal mind knowledge in the activities, so that students can get more opportunities to develop themselves and improve their own abilities. This will allow for a better identification with the work of integrating education of both.


## 3. Methods

### 3.1. Model Structure

In today’s period of rapid development of the Internet, various social media have emerged and are now widely popular. These online platforms generate a large amount of text data with emotional characteristics of reviews; for example, hotel platforms will have reviews about good and bad hotels, movie platforms will have reviews about good and bad movies, and food platforms will have reviews about good and bad food. Being able to grasp and process these emotional data provides new opportunities for enterprises to understand the ability to capture and process this sentiment data and provides new opportunities for companies to understand consumers, improve product quality, and be competitive. Sentiment analysis refers to the extraction of emotional attitudes expressed in emotionally charged texts.At present, the teaching of mental health and psychology is generally boring and by the book, and the ideological and psychological education knowledge that students learn is too formal and theoretical, which has a negative impact on the practical effect of strengthening ideological and mental health education. Therefore, combined with the actual situation of students in the new era, strengthening the organic combination of the two education methods can optimize the one sidedness of the teaching of ideological and psychological education and also make up for the singularity of the teaching of mental health education. Based on this, this paper proposes that the mental health and civics sentiment analysis model is a two-channel sentiment classification model based on BERT, channel one is composed of the BERT and CNN model, and channel two is composed of the BERT and BiLSTM-Attention model. BERT is a framework which constitutes two steps, namely, pretraining and fine-tuning. The model gets trained using unlabeled data through different pretrained tasks during the pretraining process. Then, the BERT model is initialized using pretrained parameters wherein all the parameters are fine-tuned using labeled data collected from the downstream tasks. Each of the downstream tasks has distinct fine-tuned models although they are initialized with similar pretrained models. The BERT technique has a unified architecture for all the different tasks wherein minimal difference is observed between the pretrained architecture and the final downstream architecture. BERT is basically an AI language model that enables computer systems to comprehend the meaning of texts written in ambiguous languages by surrounding texts to establish its contexts. The Bidirectional Long-Short-Term Memory (BiLSTM) is a model that helps in processing sequences. It consists of two LSTMs out of which one takes the input in forward direction and the other one in backward direction. Thus, it increases the information effectively in the network thereby improving the context of the algorithm. It helps to detect or identify the words that immediately follow or precede a particular word in a sentence. It could also be interpreted as a hybrid bidirectional LSTM and CNN architecture which learns character-level and word-level features. In the BiLSTM model, the input sequence is calculated from the opposite direction to the forward and backward hidden sequence. The study in [24] developed an integrated chatbot for the mentally ill patients. The chatbot reacts and responds compassionately using a sequence-to-sequence encoder decoder architecture. The BILSTM technique is used for the encoder, and the final results are evaluated based on the performance of the framework using the beam search and greedy search technique. The study in [25] implemented an automated system capable of detecting errors thereby facilitating effective learning and teaching among learners of Chinese as a foreign language. The traditional error detection methods primarily depended on linguistic rules and deep learning techniques. The study in [25] implemented a multichannel convolutional neural network using Bi-LSTM for detecting grammatical errors in Chinese language. The F1 score of test for Chinese as a foreign language was used to evaluate the superiority of the model, and it was found to be extremely successful in yielding promising results. The study in [26] worked on an online education platform that uses NLP pipelines. The model implements a model such as BERT to help in content curation. From the very beginning stage, pretrained language models, namely, BERT, have work extensively towards use of pretrained models for specific domains. But a specific model catering to the needs of the education system has not been developed. The model in [26] used a K12-BERT model focusing on K-12 education. The study in [27–29] developed a BERT-based model integrated with a bidirectional gated recurrent unit for feedback system structure that helped to analyze the effect of intelligent interaction between students and teachers in order to improve the curriculum. The model also performed sentimental analysis on the text using Chinese buzzwords collected from the Internet, and the results of the model justified its analytical capability. The model architecture proposed in this paper is shown in Figure 3.Figure 3
Model architecture.


### 3.2. Pretraining Model

The model in this paper is a two-channel BERT-based sentiment classification model, channel one is composed of BERT and CNN models, and channel two is composed of BERT and BiLSTM-Attention models. The problem of insufficient GPT one-way constraints is solved, and two new pretraining tasks are proposed: “masked language model” and “next sentence prediction” mean that BERT randomly masks words in a sentence with 15% probability, such as “wine” and “service” in the sentence “the best hotel in Jinan is also good.” Then, with 80% probability, we replace the masked words with [MASK], such as “the best hotel in Jinan has good service” ➝ “the best [MASK] store service is also good,” followed by a 10% probability of replacing it with a random word, such as “the best hotel service in Jinan is also good” ➝ “the best good store service in Jinan is also good.” The last 10% probability remains the same, such as “the best hotel service in Jinan is also good” ➝ “the best hotel service in Jinan is also good.” The BERT model uses a transformer architecture that uses a multiheaded attention mechanism to compute each word in a sentence in parallel, which overcomes the shortcomings of LSTM that can only process text serially but not in parallel. The core unit of the transformer encoding module is the self-attention module, which considers all other words in a sentence and decides how to encode the current word, and the calculation formula is expressed as follows.(1)AttentionQ,K,V=softmaxQKTdkV,whereQ,K,V represent the query vector, key vector, and value vector, respectively, and dk is the reconciliation factor, which is used to stabilize the gradient of module training and generally takes the value of 64. The core part of the BERT model is the transformer encoding block, and each encoding block has h attention mechanisms inside to form the multihead attention mechanism, and the schematic diagram of the multihead attention mechanism, and its computational formula is expressed as follows.
(2)Z=Concathead0,head1,⋯,headhWo,whereheadi=AttentionQWiQ,KWiK,VWiV and WiQ,WiK, are the weight matrix. In this paper, a trained model is used, which consists of 12 coding blocks, each of which has a multiheaded self-attentive operation submodule consisting of 12 heads and a word vector with an embedding dimension of 768. In the input layer, the BERT model input is a token input sequence consisting of k words, denoted as x1:k=x1,x2,⋯xi,⋯,xk, where xk refers to the i-th word, and the (CLS) token is added to the start position and the (SEP) token to the end position in the input token sequence, where SEP marks the end of a sentence and CLS represents the global feature information in the BERT model. The vector corresponding to each word entered in the BERT model consists of three vectors summed together, namely, token embeddings, segment embeddings, and position embeddings, which contain the token value corresponding to each word, the token value of a word, the position of a word, and the position of a word. The three vectors contain the token value, the sentence information, and the position information of each word. To ensure that the vectors input to the BERT model can be easily operated, the length of the input sentence token sequence in the BERT model is set to 128, and for the sequence exceeding the set maximum sequence length value, the previous sequence is saved, and the insufficient length sequence is filled with <padding>. The calculation formula for the input layer is as follows.
(3)E=ConcatEToken,ESegment,EPosition=EToken+ESegment+EPosition.The input token sequence is represented in the BERT encoding operation as follows.(4)hi=Trmhi−1,where Trm is a transformer transformation block and hi−1 indicates the output result of the current layer and the previous layer, respectively.


### 3.3. CNN Data Extraction Layer

The convolutional neural network (CNN) can effectively capture the local key feature information of text, based on the BERT model; adding the CNN model can add the acquisition of local features to the global feature information output from the BERT model, so that more feature information can be obtained. The CNN model mainly consists of a convolutional layer and pooling layer. The CNN feature schematic is shown in Figure4. After the vector H=h1,h2,⋯,hn output from the last hidden layer of the BERT model, as input to the convolutional neural network CNN inside, the input feature information is first processed as follows.
(5)hi:n=h1⊕h2⊕⋯⊕hn,where ⊕ denotes the concatenation symbol for concatenating the vectors of the CNN input and hn denotes the concatenation. The spliced data is input to the convolutional layer for convolutional operation. The filter of the convolutional layer is w∈Rp×k, the word vector dimension of the filter width setting is k, and the height setting is p. That is, the convolutional operation is performed between p neighboring words in the sentence at a time to extract the n-gram features of the text. If the features obtained from the input layer after intercepting the word vector hiip:1 by the filter are ci, the extraction of one feature is represented as follows:
(6)ci=fw·hi:i+p−1+b,Figure 4
CNN feature schematic.whereb denotes the bias term and f is the nonlinear activation function. The convolution kernel slides over the word vector matrix of the input layer, and the generated feature mapping is
(7)c=c1,c2,⋯,cn−p+1.After that, the maximum pooling operation is performed onc∈Rn−p+1, so that the largest feature inside the extracted feature vector replaces the whole feature vector. As shown in the rightmost partial enlargement, assuming that the maximum pooling operation is performed for 4, 5, 8, and 7 in the other box, the value obtained after the maximum pooling operation is 8. The formula for the pooling operation is expressed as follows.
(8)c^=maxc.Finally, thec obtained from all filters is pooled as follows.
(9)U=c^1,c^2,c^3.The matrixU is stitched together by three different features c^1,c^2,c^3, obtained by convolving three different-sized convolution kernels of 2, 3, and 4 after the convolution operation and then after the maximum pooling layer.


### 3.4. BiLSTM

LSTM is one of the RNN variants of recurrent neural networks. The cell is added to LSTM, and the memory function is realized by controlling the transmission state through gating state, which solves the problem of gradient disappearance and gradient explosion caused by RNN due to the long text sequence. The LSTM network cell consists of an input gate, forget gate, and output gate. The forgetting gate determines which information is discarded from the cell state at the previous moment, taking the current moment inputxt and the previous moment hidden layer output ht1 as input, which is expressed by the following equation:
(10)ft=σWfht−1,xt+bf.Wf is the weight factor, and bf is the bias amount. The input gate determines how much of the input information needs to be retained and updates the information that needs to be retained into the current neural network unit, which is expressed by the following formula.
(11)it=σWiht−1,xt+bi,C~t=tanhWCht−1,xt+bC,ot=σWoht−1,xt+bo,ht=ot×tanhCt,where σ is the sigmoid function used to calculate which information needs to be updated to get its value, and the tanh function generates a vector Ct to temporarily store the alternative information for updating. After the input and forgetting gates, the current cell state is determined by the combination of the cell state Ct and the input gate update state Ct at the previous moment.
(12)l⟶t=LSTM⟶l⟶t−1,ht.The output gate controls which information is output in the current neural network cell to the next neural cell. Here, the sigmoid function is used to determine which information is output, and then, the tanh function is used to process the current cell state and multiply it with the output gate to obtain the current moment hidden layer state.


### 3.5. Attention Mechanism

The attention mechanism is to extract the information related to the target among many information, and the vectors output by the BiLSTM layer have various characteristics, and each vector has a different degree of influence on the classification result; the purpose of adding the attention mechanism in this paper is to focus on the vector information that affects the classification result and to assign more weight to the vector information that affects the classification result, so that it takes the absolute advantage in the output vectors. A schematic diagram of the attention mechanism is shown in Figure5. The attention mechanism operation is done on the vectors output from the BiLSTM layer, and the formula is expressed as follows.
(13)ut=tanhwtlt+bt,αt=exput∑k=1nexpuk,F=∑t=1nαtlt.Figure 5
Schematic diagram of the attention mechanism.lt denotes the feature vector output by BiLSTM at moment t, bt denotes the bias, wt denotes the weight matrix of lt, ut denotes the hidden layer representation obtained for lt by tanh neural network calculation, t denotes the weight obtained by the softmax function, which records the size value of the influence of a vector on the classification result, and F denotes the weighted operation after This feature vector focuses on the feature information that contains high impact on the classification result.


### 3.6. Output Layer

The word vector information output from the hidden layer of the BERT model is stitched together after two channels of CNN and BiLSTM-Attention, each retaining more feature information which extends the feature information output from the BERT model by adding local feature information, long-distance feature information, and feature information with high impact on the classification result. Then, input to the fully connected layer, and finally output the category information through the classification operation by softmax function, which is expressed by the formula as follows.(14)O=U⊕F,PyiO=softmaxWoO,whereyi denotes the classification output result, i=1,2,⋯,k; k denotes the classification category, and Wo is the weight matrix.


## 3.1. Model Structure

In today’s period of rapid development of the Internet, various social media have emerged and are now widely popular. These online platforms generate a large amount of text data with emotional characteristics of reviews; for example, hotel platforms will have reviews about good and bad hotels, movie platforms will have reviews about good and bad movies, and food platforms will have reviews about good and bad food. Being able to grasp and process these emotional data provides new opportunities for enterprises to understand the ability to capture and process this sentiment data and provides new opportunities for companies to understand consumers, improve product quality, and be competitive. Sentiment analysis refers to the extraction of emotional attitudes expressed in emotionally charged texts.At present, the teaching of mental health and psychology is generally boring and by the book, and the ideological and psychological education knowledge that students learn is too formal and theoretical, which has a negative impact on the practical effect of strengthening ideological and mental health education. Therefore, combined with the actual situation of students in the new era, strengthening the organic combination of the two education methods can optimize the one sidedness of the teaching of ideological and psychological education and also make up for the singularity of the teaching of mental health education. Based on this, this paper proposes that the mental health and civics sentiment analysis model is a two-channel sentiment classification model based on BERT, channel one is composed of the BERT and CNN model, and channel two is composed of the BERT and BiLSTM-Attention model. BERT is a framework which constitutes two steps, namely, pretraining and fine-tuning. The model gets trained using unlabeled data through different pretrained tasks during the pretraining process. Then, the BERT model is initialized using pretrained parameters wherein all the parameters are fine-tuned using labeled data collected from the downstream tasks. Each of the downstream tasks has distinct fine-tuned models although they are initialized with similar pretrained models. The BERT technique has a unified architecture for all the different tasks wherein minimal difference is observed between the pretrained architecture and the final downstream architecture. BERT is basically an AI language model that enables computer systems to comprehend the meaning of texts written in ambiguous languages by surrounding texts to establish its contexts. The Bidirectional Long-Short-Term Memory (BiLSTM) is a model that helps in processing sequences. It consists of two LSTMs out of which one takes the input in forward direction and the other one in backward direction. Thus, it increases the information effectively in the network thereby improving the context of the algorithm. It helps to detect or identify the words that immediately follow or precede a particular word in a sentence. It could also be interpreted as a hybrid bidirectional LSTM and CNN architecture which learns character-level and word-level features. In the BiLSTM model, the input sequence is calculated from the opposite direction to the forward and backward hidden sequence. The study in [24] developed an integrated chatbot for the mentally ill patients. The chatbot reacts and responds compassionately using a sequence-to-sequence encoder decoder architecture. The BILSTM technique is used for the encoder, and the final results are evaluated based on the performance of the framework using the beam search and greedy search technique. The study in [25] implemented an automated system capable of detecting errors thereby facilitating effective learning and teaching among learners of Chinese as a foreign language. The traditional error detection methods primarily depended on linguistic rules and deep learning techniques. The study in [25] implemented a multichannel convolutional neural network using Bi-LSTM for detecting grammatical errors in Chinese language. The F1 score of test for Chinese as a foreign language was used to evaluate the superiority of the model, and it was found to be extremely successful in yielding promising results. The study in [26] worked on an online education platform that uses NLP pipelines. The model implements a model such as BERT to help in content curation. From the very beginning stage, pretrained language models, namely, BERT, have work extensively towards use of pretrained models for specific domains. But a specific model catering to the needs of the education system has not been developed. The model in [26] used a K12-BERT model focusing on K-12 education. The study in [27–29] developed a BERT-based model integrated with a bidirectional gated recurrent unit for feedback system structure that helped to analyze the effect of intelligent interaction between students and teachers in order to improve the curriculum. The model also performed sentimental analysis on the text using Chinese buzzwords collected from the Internet, and the results of the model justified its analytical capability. The model architecture proposed in this paper is shown in Figure 3.Figure 3
Model architecture.


## 3.2. Pretraining Model

The model in this paper is a two-channel BERT-based sentiment classification model, channel one is composed of BERT and CNN models, and channel two is composed of BERT and BiLSTM-Attention models. The problem of insufficient GPT one-way constraints is solved, and two new pretraining tasks are proposed: “masked language model” and “next sentence prediction” mean that BERT randomly masks words in a sentence with 15% probability, such as “wine” and “service” in the sentence “the best hotel in Jinan is also good.” Then, with 80% probability, we replace the masked words with [MASK], such as “the best hotel in Jinan has good service” ➝ “the best [MASK] store service is also good,” followed by a 10% probability of replacing it with a random word, such as “the best hotel service in Jinan is also good” ➝ “the best good store service in Jinan is also good.” The last 10% probability remains the same, such as “the best hotel service in Jinan is also good” ➝ “the best hotel service in Jinan is also good.” The BERT model uses a transformer architecture that uses a multiheaded attention mechanism to compute each word in a sentence in parallel, which overcomes the shortcomings of LSTM that can only process text serially but not in parallel. The core unit of the transformer encoding module is the self-attention module, which considers all other words in a sentence and decides how to encode the current word, and the calculation formula is expressed as follows.(1)AttentionQ,K,V=softmaxQKTdkV,whereQ,K,V represent the query vector, key vector, and value vector, respectively, and dk is the reconciliation factor, which is used to stabilize the gradient of module training and generally takes the value of 64. The core part of the BERT model is the transformer encoding block, and each encoding block has h attention mechanisms inside to form the multihead attention mechanism, and the schematic diagram of the multihead attention mechanism, and its computational formula is expressed as follows.
(2)Z=Concathead0,head1,⋯,headhWo,whereheadi=AttentionQWiQ,KWiK,VWiV and WiQ,WiK, are the weight matrix. In this paper, a trained model is used, which consists of 12 coding blocks, each of which has a multiheaded self-attentive operation submodule consisting of 12 heads and a word vector with an embedding dimension of 768. In the input layer, the BERT model input is a token input sequence consisting of k words, denoted as x1:k=x1,x2,⋯xi,⋯,xk, where xk refers to the i-th word, and the (CLS) token is added to the start position and the (SEP) token to the end position in the input token sequence, where SEP marks the end of a sentence and CLS represents the global feature information in the BERT model. The vector corresponding to each word entered in the BERT model consists of three vectors summed together, namely, token embeddings, segment embeddings, and position embeddings, which contain the token value corresponding to each word, the token value of a word, the position of a word, and the position of a word. The three vectors contain the token value, the sentence information, and the position information of each word. To ensure that the vectors input to the BERT model can be easily operated, the length of the input sentence token sequence in the BERT model is set to 128, and for the sequence exceeding the set maximum sequence length value, the previous sequence is saved, and the insufficient length sequence is filled with <padding>. The calculation formula for the input layer is as follows.
(3)E=ConcatEToken,ESegment,EPosition=EToken+ESegment+EPosition.The input token sequence is represented in the BERT encoding operation as follows.(4)hi=Trmhi−1,where Trm is a transformer transformation block and hi−1 indicates the output result of the current layer and the previous layer, respectively.


## 3.3. CNN Data Extraction Layer

The convolutional neural network (CNN) can effectively capture the local key feature information of text, based on the BERT model; adding the CNN model can add the acquisition of local features to the global feature information output from the BERT model, so that more feature information can be obtained. The CNN model mainly consists of a convolutional layer and pooling layer. The CNN feature schematic is shown in Figure4. After the vector H=h1,h2,⋯,hn output from the last hidden layer of the BERT model, as input to the convolutional neural network CNN inside, the input feature information is first processed as follows.
(5)hi:n=h1⊕h2⊕⋯⊕hn,where ⊕ denotes the concatenation symbol for concatenating the vectors of the CNN input and hn denotes the concatenation. The spliced data is input to the convolutional layer for convolutional operation. The filter of the convolutional layer is w∈Rp×k, the word vector dimension of the filter width setting is k, and the height setting is p. That is, the convolutional operation is performed between p neighboring words in the sentence at a time to extract the n-gram features of the text. If the features obtained from the input layer after intercepting the word vector hiip:1 by the filter are ci, the extraction of one feature is represented as follows:
(6)ci=fw·hi:i+p−1+b,Figure 4
CNN feature schematic.whereb denotes the bias term and f is the nonlinear activation function. The convolution kernel slides over the word vector matrix of the input layer, and the generated feature mapping is
(7)c=c1,c2,⋯,cn−p+1.After that, the maximum pooling operation is performed onc∈Rn−p+1, so that the largest feature inside the extracted feature vector replaces the whole feature vector. As shown in the rightmost partial enlargement, assuming that the maximum pooling operation is performed for 4, 5, 8, and 7 in the other box, the value obtained after the maximum pooling operation is 8. The formula for the pooling operation is expressed as follows.
(8)c^=maxc.Finally, thec obtained from all filters is pooled as follows.
(9)U=c^1,c^2,c^3.The matrixU is stitched together by three different features c^1,c^2,c^3, obtained by convolving three different-sized convolution kernels of 2, 3, and 4 after the convolution operation and then after the maximum pooling layer.


## 3.4. BiLSTM

LSTM is one of the RNN variants of recurrent neural networks. The cell is added to LSTM, and the memory function is realized by controlling the transmission state through gating state, which solves the problem of gradient disappearance and gradient explosion caused by RNN due to the long text sequence. The LSTM network cell consists of an input gate, forget gate, and output gate. The forgetting gate determines which information is discarded from the cell state at the previous moment, taking the current moment inputxt and the previous moment hidden layer output ht1 as input, which is expressed by the following equation:
(10)ft=σWfht−1,xt+bf.Wf is the weight factor, and bf is the bias amount. The input gate determines how much of the input information needs to be retained and updates the information that needs to be retained into the current neural network unit, which is expressed by the following formula.
(11)it=σWiht−1,xt+bi,C~t=tanhWCht−1,xt+bC,ot=σWoht−1,xt+bo,ht=ot×tanhCt,where σ is the sigmoid function used to calculate which information needs to be updated to get its value, and the tanh function generates a vector Ct to temporarily store the alternative information for updating. After the input and forgetting gates, the current cell state is determined by the combination of the cell state Ct and the input gate update state Ct at the previous moment.
(12)l⟶t=LSTM⟶l⟶t−1,ht.The output gate controls which information is output in the current neural network cell to the next neural cell. Here, the sigmoid function is used to determine which information is output, and then, the tanh function is used to process the current cell state and multiply it with the output gate to obtain the current moment hidden layer state.


## 3.5. Attention Mechanism

The attention mechanism is to extract the information related to the target among many information, and the vectors output by the BiLSTM layer have various characteristics, and each vector has a different degree of influence on the classification result; the purpose of adding the attention mechanism in this paper is to focus on the vector information that affects the classification result and to assign more weight to the vector information that affects the classification result, so that it takes the absolute advantage in the output vectors. A schematic diagram of the attention mechanism is shown in Figure5. The attention mechanism operation is done on the vectors output from the BiLSTM layer, and the formula is expressed as follows.
(13)ut=tanhwtlt+bt,αt=exput∑k=1nexpuk,F=∑t=1nαtlt.Figure 5
Schematic diagram of the attention mechanism.lt denotes the feature vector output by BiLSTM at moment t, bt denotes the bias, wt denotes the weight matrix of lt, ut denotes the hidden layer representation obtained for lt by tanh neural network calculation, t denotes the weight obtained by the softmax function, which records the size value of the influence of a vector on the classification result, and F denotes the weighted operation after This feature vector focuses on the feature information that contains high impact on the classification result.


## 3.6. Output Layer

The word vector information output from the hidden layer of the BERT model is stitched together after two channels of CNN and BiLSTM-Attention, each retaining more feature information which extends the feature information output from the BERT model by adding local feature information, long-distance feature information, and feature information with high impact on the classification result. Then, input to the fully connected layer, and finally output the category information through the classification operation by softmax function, which is expressed by the formula as follows.(14)O=U⊕F,PyiO=softmaxWoO,whereyi denotes the classification output result, i=1,2,⋯,k; k denotes the classification category, and Wo is the weight matrix.


## 4. Experiments and Results

### 4.1. Dataset

The mental health and ideology classification dataset used in this paper uses both Chinese and English datasets, and the Chinese dataset uses a larger dataset of mental health and ideology text comments provided by a research institution in China. The dataset can provide a certain platform for Chinese sentiment analysis, with a total of 6000 data, 3000 positive and negative comments of emotional polarity each, which are preprocessed in this paper by jieba splitting, removing some deactivated words, etc. Some samples and the average length of the preprocessed data are about 125. The English dataset is used from the 2015 Yelp Dataset Challenge dataset, which contains 280,000 training samples and 19,000 test samples with different polarities of positive and negative, respectively, with an average length of about 109.


### 4.2. Experimental Setup

The experiments in this paper are done on PyTorch 1.4.0 deep learning framework, the operating system is 64-bit Ubuntu20.04, and the GPU is 24 G memory INVDAQuadroRTX6000. The comparison models used in this paper are CNN and BiLSTM for Chinese using Sogou News’ word character pretraining word model and English using Google’s word2vec pretraining word model, both with dimension 300d. The model in this paper and the comparison model with BERT word vector embedding layer are BERT-base-Chinese for Chinese and BERT-base-uncased for English, and the experimental parameters of this paper are shown in Table1. The experimental parameters of this paper are shown in Table 1. The diagram of the training process performance improvement is shown in Figure 6.Table 1
Model parameter setting.
Parameter nameSet valueNum classes2Max_epochs20Batch size32Seq_length128Dropout0.1Learning_rate2e−5Num filters256Filter sizes(2, 3, 4)Hidden size768Figure 6
Schematic diagram of performance enhancement during training.


### 4.3. Evaluation Metrics

In this paper, accuracy, precision, recall, and F1 values are used to evaluate the model, and these metrics can fairly evaluate the performance of the model, and they are defined as follows: the pretraining model used for the comparison model in Chinese is BERT-base-Chinese, and the one used in English is BERT-base-uncased, with the experimental parameters of the model. TP denotes the number of samples predicted to be positive in positive sentiment samples, FP denotes the number of samples predicted to be positive in negative sentiment samples, FN denotes the number of samples predicted to be negative in positive sentiment samples, and TN denotes the number of samples predicted to be negative in negative sentiment samples.


### 4.4. Experimental Results

First, this paper compares with the following some currently popular sentiment classification models, comparing the models mainly with CNN, BiLSTM, BiLSTM-Attention, RCNN, DPCNN, LDA+self-attention, etc., with the experimental results of LDA + self-attention model. The test results of the models in this paper with these models are shown in Table2. It can be seen that the accuracy value during training converges faster and the change floating after convergence is small compared with these comparison models above, which are affected by factors such as the size of the dataset data, and in the Chinese dataset with small data volume, the accuracy value is trained by CNN, BiLSTM, and other models in the English Yelp dataset with a large amount of data, and the variation of the accuracy value after convergence is relatively small. Also comparing the test results in Table 2, we can see that this model outperforms CNN, BiLSTM, BiLSTM-Attention, RCNN, DPCNN, and LDA+self-attention models in all four evaluation indexes and outperforms the best-performing model among the comparison models in the Chinese dataset. The LDA+self-attention model outperforms the best performing BiLSTM-Attention model among the comparison models in the English Yelp dataset by 3.59%, 2.34%, and 3.16% in accuracy A, recall R, and F1 values, respectively, and by 2.74%, 2.73%, 2.74%, and 2.74% in the English Yelp dataset. In summary, the model in this paper performs better in sentiment classification compared to the current popular sentiment classification models.Table 2
Test results of different emotion classification models.
ModelsChineseYelpAPRF1APRF1CNN0.88000.88010.88000.88000.90950.90950.90950.9095BiLSTM0.86980.87200.87000.87100.92630.92630.92630.9263BiLSTM-Attention0.87300.87710.87330.87520.92810.92820.92810.9281RCNN0.88330.88340.88330.88330.92070.92080.92070.9207DPCNN0.85140.85370.85170.85270.92010.92050.92020.9201LDA+self-attention0.8924—0.90490.8970————Proposed model0.92830.92890.92830.92860.95550.95550.95550.9555In this paper, we adopt the BERT dynamic pretraining model as the word vector embedding layer of this model, and the word vectors obtained by this model are dynamic; each word vector has dynamic and close connection with other word vectors in the context, and the word vectors generated by different words can change well according to the surrounding environment, while Word2Vec and other models generate word vectors that are fixed. The problem of multiple meanings of one word is not solved. The BERT word vector embedding layer of this paper’s model was removed and replaced using the Word2Vec word vector embedding layer, and the experimental results of the test are shown in Table3. CBLA represents a model consisting of two channels, CNN and BiLSTM-Attention, and Word2Vec+CBLA indicates that the word vector embedding layer of the CBLA two-channel model is Word2Vec model; it can be seen that the model using the BERT model as the word vector embedding layer in this paper converges faster in the trained accuracy values and the change floats less after convergence compared with the Word2vec+CBLA model using Word2Vec as the word vector embedding layer, and from the test results in Table 3, it can be seen that the model is significantly higher than the Word2vec+CBLA model in the Chinese and English Yelp datasets in terms of accuracy A, precision P, recall R, and F1 value test results, and the four index values in the Chinese dataset are 3.83%, 3.85%, 3.83%, 3.85%, 3.83%, and 3.84%, respectively, on the Chinese dataset, and 2.51%, 2.50%, 2.51% and 2.51%, respectively, on the English Yelp dataset. In summary, the model with BERT dynamic word vectors is better than the Word2Vec+CBLA model with Word2Vec static word vectors for sentiment classification, indicating that the dynamic word vector model has richer word vector feature information than the word vector trained by the static word vector model.Table 3
Test results with different pretrained models.
ModelsChineseYelpAPRF1APRF1Word2vec+CBLA0.89000.89000.93040.93050.89040.89020.93040.9304Proposed model0.92830.92890.92860.95550.95550.95550.95550.9283


## 4.1. Dataset

The mental health and ideology classification dataset used in this paper uses both Chinese and English datasets, and the Chinese dataset uses a larger dataset of mental health and ideology text comments provided by a research institution in China. The dataset can provide a certain platform for Chinese sentiment analysis, with a total of 6000 data, 3000 positive and negative comments of emotional polarity each, which are preprocessed in this paper by jieba splitting, removing some deactivated words, etc. Some samples and the average length of the preprocessed data are about 125. The English dataset is used from the 2015 Yelp Dataset Challenge dataset, which contains 280,000 training samples and 19,000 test samples with different polarities of positive and negative, respectively, with an average length of about 109.


## 4.2. Experimental Setup

The experiments in this paper are done on PyTorch 1.4.0 deep learning framework, the operating system is 64-bit Ubuntu20.04, and the GPU is 24 G memory INVDAQuadroRTX6000. The comparison models used in this paper are CNN and BiLSTM for Chinese using Sogou News’ word character pretraining word model and English using Google’s word2vec pretraining word model, both with dimension 300d. The model in this paper and the comparison model with BERT word vector embedding layer are BERT-base-Chinese for Chinese and BERT-base-uncased for English, and the experimental parameters of this paper are shown in Table1. The experimental parameters of this paper are shown in Table 1. The diagram of the training process performance improvement is shown in Figure 6.Table 1
Model parameter setting.
Parameter nameSet valueNum classes2Max_epochs20Batch size32Seq_length128Dropout0.1Learning_rate2e−5Num filters256Filter sizes(2, 3, 4)Hidden size768Figure 6
Schematic diagram of performance enhancement during training.


## 4.3. Evaluation Metrics

In this paper, accuracy, precision, recall, and F1 values are used to evaluate the model, and these metrics can fairly evaluate the performance of the model, and they are defined as follows: the pretraining model used for the comparison model in Chinese is BERT-base-Chinese, and the one used in English is BERT-base-uncased, with the experimental parameters of the model. TP denotes the number of samples predicted to be positive in positive sentiment samples, FP denotes the number of samples predicted to be positive in negative sentiment samples, FN denotes the number of samples predicted to be negative in positive sentiment samples, and TN denotes the number of samples predicted to be negative in negative sentiment samples.


## 4.4. Experimental Results

First, this paper compares with the following some currently popular sentiment classification models, comparing the models mainly with CNN, BiLSTM, BiLSTM-Attention, RCNN, DPCNN, LDA+self-attention, etc., with the experimental results of LDA + self-attention model. The test results of the models in this paper with these models are shown in Table2. It can be seen that the accuracy value during training converges faster and the change floating after convergence is small compared with these comparison models above, which are affected by factors such as the size of the dataset data, and in the Chinese dataset with small data volume, the accuracy value is trained by CNN, BiLSTM, and other models in the English Yelp dataset with a large amount of data, and the variation of the accuracy value after convergence is relatively small. Also comparing the test results in Table 2, we can see that this model outperforms CNN, BiLSTM, BiLSTM-Attention, RCNN, DPCNN, and LDA+self-attention models in all four evaluation indexes and outperforms the best-performing model among the comparison models in the Chinese dataset. The LDA+self-attention model outperforms the best performing BiLSTM-Attention model among the comparison models in the English Yelp dataset by 3.59%, 2.34%, and 3.16% in accuracy A, recall R, and F1 values, respectively, and by 2.74%, 2.73%, 2.74%, and 2.74% in the English Yelp dataset. In summary, the model in this paper performs better in sentiment classification compared to the current popular sentiment classification models.Table 2
Test results of different emotion classification models.
ModelsChineseYelpAPRF1APRF1CNN0.88000.88010.88000.88000.90950.90950.90950.9095BiLSTM0.86980.87200.87000.87100.92630.92630.92630.9263BiLSTM-Attention0.87300.87710.87330.87520.92810.92820.92810.9281RCNN0.88330.88340.88330.88330.92070.92080.92070.9207DPCNN0.85140.85370.85170.85270.92010.92050.92020.9201LDA+self-attention0.8924—0.90490.8970————Proposed model0.92830.92890.92830.92860.95550.95550.95550.9555In this paper, we adopt the BERT dynamic pretraining model as the word vector embedding layer of this model, and the word vectors obtained by this model are dynamic; each word vector has dynamic and close connection with other word vectors in the context, and the word vectors generated by different words can change well according to the surrounding environment, while Word2Vec and other models generate word vectors that are fixed. The problem of multiple meanings of one word is not solved. The BERT word vector embedding layer of this paper’s model was removed and replaced using the Word2Vec word vector embedding layer, and the experimental results of the test are shown in Table3. CBLA represents a model consisting of two channels, CNN and BiLSTM-Attention, and Word2Vec+CBLA indicates that the word vector embedding layer of the CBLA two-channel model is Word2Vec model; it can be seen that the model using the BERT model as the word vector embedding layer in this paper converges faster in the trained accuracy values and the change floats less after convergence compared with the Word2vec+CBLA model using Word2Vec as the word vector embedding layer, and from the test results in Table 3, it can be seen that the model is significantly higher than the Word2vec+CBLA model in the Chinese and English Yelp datasets in terms of accuracy A, precision P, recall R, and F1 value test results, and the four index values in the Chinese dataset are 3.83%, 3.85%, 3.83%, 3.85%, 3.83%, and 3.84%, respectively, on the Chinese dataset, and 2.51%, 2.50%, 2.51% and 2.51%, respectively, on the English Yelp dataset. In summary, the model with BERT dynamic word vectors is better than the Word2Vec+CBLA model with Word2Vec static word vectors for sentiment classification, indicating that the dynamic word vector model has richer word vector feature information than the word vector trained by the static word vector model.Table 3
Test results with different pretrained models.
ModelsChineseYelpAPRF1APRF1Word2vec+CBLA0.89000.89000.93040.93050.89040.89020.93040.9304Proposed model0.92830.92890.92860.95550.95550.95550.95550.9283


## 5. Conclusion

The effective implementation and enforcement of curriculum thinking politics in the teaching of college students’ mental normalcy can not only promote the effectiveness of college students’ mental normalcy education but also has great significance for the development of college students’ physical and mental health. Therefore, the educators of college students should strengthen the attention and research on the curriculum of the normal education and combine the actual needs of the psychological development of college students, and reasonably integrate the curriculum of the normal education into all aspects of the normal education, so as to promote the innovation of the normal education of college students, make it better serve for the normal development of college students’ minds, promote the normal development of students’ minds, and cultivate more high-quality and comprehensive talents for the socialist construction of China. In this paper, we propose a multichannel model fusion emotion classification model using a static pretraining model and propose a dynamic pretraining model based on BERT as a two-channel emotion text classification model with a word vector embedding layer, which is designed to compare with the current one. The study yields promising results but is dependent on the BERT technique which often lags interpretability, traceability, and justification of the results when new data are fed into the framework. This challenge could be overcome with the use of explainable AI ensuring enhanced interpretability and traceability.



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*Source: 5378694-2022-08-02.xml*