# Research on the Reconstruction Strategy of Public English Ecological Classroom under the Background of “Internet +”

Authors: Yanqiu Hu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

In the era of “Internet plus,” the educational ecosystem is changing under the influence of modern educational technology, with teachers and students as the main players. The traditional teaching method of “books + classroom + large-class teaching” with teachers at the center has evolved into “two masters: teachers leading and students as the main body.” There are also some discussions in foreign language teaching about educational ecology, with a focus on ecological imbalance. Emotion analysis is divided into two categories based on emotional information: positive and negative. It can be considered a unique text classification, despite the fact that it differs from traditional text classification. Using the emotion recognition algorithm, this paper can provide simple access to WEB pages and carry out emotional experiences such as face emotion recognition. Third-party developers, on the other hand, have a lower entry barrier, allowing them to quickly create colorful applications based on their own ideas.


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## Body

## 1. Introduction

Internet technology has sparked a revolution in the field of education [1], relying on the technical support of cloud computing, big data, mobile Internet, and the Internet thinking of “free access.” For many years, public English ecological classrooms have been conducted in the research field both at home and abroad. Looking at its research, it is clear that this field's research characteristics are primarily manifested in two ways: one, from macro to microresearch and the other, from theoretical discussion to practical analysis. The educational ecosystem, with teachers and students as the main bodies, is changing under the influence of modern education technology in the Internet Plus era. The “traditional books + Classroom + large class teaching” passive teaching mode is transformed into “two main: teacher led, student oriented” teaching modes [2]. There are also some discussions on education ecology in foreign language teaching, which primarily focus on the phenomena and causes of ecological imbalance, niche positioning, the impact of the network environment on the ecological classroom, and so on, but the related research, particularly in practical teaching, is not sufficiently deep [3]. Simultaneously, it has resulted in a slew of new ecological imbalance issues in English classroom teaching, such as “alienation of teacher-student relationships, low information literacy of teachers and students, disunity of teaching behavior and ideas, and imbalance of information-based teaching environment construction,” all of which have harmed the reform effect of English information-based teaching in higher vocational colleges [4]. As a result, educators should consider how to correct the imbalance caused by the use of modern information technology in English teaching and how to restore the ecological balance of the English classroom from a new perspective of educational ecology in an era when “Internet plus” brings both opportunities and challenges to English teaching [5].Emotion is a significant factor in the development of a healthy personality in students. Good emotion can pique a person's interest, which can then be translated into a desire to learn. With the growth of the Internet, natural language processing technology, and the demand created by the increased availability of Internet information, there are an increasing number of studies on emotion analysis [6,7]. According to affective information, affective analysis is primarily divided into positive and negative meanings. Although it differs from traditional text classification, it can be considered a unique text classification. The attitude tendency expressed by the text, rather than some facts, is the classification standard of affective analysis [8,9]. This paper proposes an emotion recognition algorithm based on emotion analysis. Emotion recognition is one aspect of emotion computing, and its research is mostly based on algorithms with a high entry threshold, which drives up the cost of development and promotion for ordinary developers. Students' interest in something can be gradually transferred under the pouring effect of emotion analysis, thus improving students' cognitive function. An emotion recognition algorithm plays an important role in personal emotion analysis as a teaching method that can guide the strength and nature of personal emotion analysis. As a result, key emotion recognition technologies and services can be packaged into a set of application programming interfaces (APIs) and made available to third-party developers [10]. “Analyzing subjective texts and making decisions based on them” was the original definition of emotion analysis. Das Chen and Tong [11] used the emotion recognition method to investigate the reconstruction strategies of a public English ecological classroom.On the one hand, it can provide simple access to web pages and emotional experiences such as face emotion recognition through the emotion recognition algorithm; on the other hand, it lowers the access threshold for third-party developers, allowing them to express their creativity and develop rich and colourful applications based on the platform [12,13]. Reconstruction strategies based on emotion recognition algorithms in public English ecological classrooms are mostly limited to a few fields. Some hierarchical models and rules without training are established by combining the psychological research results in some specific fields to build the reconstruction strategies of the public English ecological classroom and complete the task of emotion classification [14]. To realise expression recognition, the open platform of emotion recognition algorithms primarily uses the reconstruction strategy of the public English ecological classroom against the backdrop of “Internet +.” The research direction of the restructuring strategy in public English ecological classrooms is “Internet plus.” It has made significant progress in speech recognition, computer vision, and other areas in recent years. When inputting the reconstruction strategy of a public English ecological classroom, an emotion recognition algorithm can use “Internet plus” as the original input. It requires very little preprocessing, which simplifies the network structure.


## 2. Related Work

The problems encountered during the implementation process are considered from the perspective of the name of the public English ecological classroom, the form of the public English ecological classroom, the construction of public English ecological classroom teaching materials, and public English ecological classroom teaching against the backdrop of “Internet +,” according to the literature [15]. The implementation process of a public English ecological classroom under the background of “Internet +” can mainly cover five issues, namely, “five levels of classroom sustainability, conceptual basis, philosophical orientation, the relationship between integration and independence, and evaluation methods,” according to the literature [16]. The multidimensional and multiparameter system view, the reconstruction strategy and coordinated development view of education, and the public English ecological classroom are regarded as the most fundamental views of educational ecology, with a slightly strong ecological color, according to literature [17] studies. According to the literature [18], public English ecology primarily studies the relationship between the interaction and interaction of educational ecosystems and discusses strategies, ways, and methods to realise the reconstruction of the best educational ecological structure in order to continuously improve educational benefits and promote the growth of all types of talents. Literature [19] as seen through the lens of a public English ecological classroom in the context of “Internet +” using the big data analysis method. The relationship between public English ecological classroom and reconstruction strategies is discussed in this paper. According to the literature [20], research on the ecological classroom ecology of public English in the context of “Internet +” also includes research on reconstruction strategies for macroeducational ecological problems like education, culture, and ecological crisis. Some thoughts on reconstruction strategies in the implementation of public English ecological classrooms in the context of “Internet +” are mentioned in the literature [21]. [22] According to previous research on public English ecological classrooms under the background of “Internet plus,” “attention used to focus on individual students' experiences and school achievements, but now it has turned to a series of factors that constitute the reconstruction strategy of students' public English ecological classroom” [23]. According to research, the public English ecological classroom should be investigated in the context of the current environment and a development reconstruction strategy that pays attention to the world, especially in light of “Internet plus.” According to the literature [24], there are several major aspects of the transition from public English ecological classroom education to public English restructuring strategy education in the context of “Internet plus,” including classroom objectives, content, methods, and evaluation, among others.This paper introduces the emotion recognition algorithm in emotion analysis, examines the public English ecological classroom's reconstruction strategy against the backdrop of “Internet +,” and concludes that the emotion recognition algorithm can provide simple access to web pages and emotional experiences such as face emotion recognition. On the other hand, it lowers the barrier to entry for third-party developers, allowing them to express their creativity and create rich, colourful applications using the platform.


## 3. Principle and Model Method of the Emotion Recognition Algorithm

The emotion recognition open platform uses the core technologies in the fields of big data, deep learning, artificial intelligence, emotion computing, and mobile Internet, and proposes new methods and applications such as emotion recognition, emotion analysis and expression, emotion modeling, and emotion interaction, based on the scientific research strength of South China University of Technology's machine learning and data mining laboratory. The emotion analysis classification module primarily analyzes the emotions of the comment information crawled by the web crawler module and divides it into three categories: commendatory, neutral, and derogatory. Based on the perspective of the public English ecological classroom under the background of “Internet +,” the public English ecological classroom can be regarded as a special microecological environment. Teachers, students, and the environment are the ecological factors. The main body of the classroom ecology is made up of teachers and students. The two basic elements of the ecological classroom are the main body of the public English ecological classroom and the classroom ecological environment. On-site evaluation and online experience value evaluation of cloud platforms are part of the public English ecological classroom reconstruction strategy set against the backdrop of “Internet +.” It is a crucial link in the emotional analysis ecological classroom's practice. In order to provide timely feedback adjustment, on-the-spot evaluation of public English ecological classrooms must implement evaluation elements, make clear the key points of feedback emotion analysis and adjustment, and edit the evaluation items and standards of public English classrooms according to groups and individual categories. The most popular emotional analysis and evaluation method at the moment is a combination of process and summative evaluation, with the emphasis on process evaluation and evaluation subjects including students, teachers, and peers. The public English ecological classroom submodule and the combined classification submodule based on the emotion recognition algorithm are both included in this module. The entire system is broken down into four modules, as shown in Figure1.Figure 1
Emotion recognition algorithm system module under emotion analysis.The extraction of sentiment analysis words is critical in the process of emotion recognition algorithms. It is difficult to accurately judge the sentiment analysis tendency of sentences without accurate sentiment analysis words, so it is necessary to accurately extract sentiment analysis words from sentences. Teachers guide students in a public English ecological classroom called “Life, Life, Survival, and Vitality,” which focuses on students' subjective development, future career development, stimulating students' interest in learning, cultivating students' self-confidence, paying attention to output, providing students with more language practice opportunities, and creating a language ecological environment for students to show themselves. To achieve “scattered form and spirit,” teachers should reconstruct the new curriculum content system before the emotional analysis class. Curriculum content should be chosen in accordance with professional training objectives and the needs of students' personal development, which will be a relatively large project in teachers' teaching work. The specific functions of the emotion recognition algorithm platform are determined after analyzing the requirements and consulting the major mainstream open platforms at home and abroad, as shown in Figure2.Figure 2
Functional structure of the emotion recognition algorithm.In the field of emotion analysis, the emotion recognition algorithm is a widely used method. It is the first time a multilayer network structure has been successfully trained using a learning method. A multilayer perceptron artificial neural network and an emotion recognition algorithm are used to create the emotion analysis. As part of a multilayer perceptron, each emotion analysis layer, emotion recognition, and incentive function form a new layer. There are a lot of public English classes, and the teachers' workload is heavy, and they are influenced by traditional educational concepts. The teacher-centered unilateral input mode is commonly used in public English classrooms. The dominant position of students is not prominent. Students are still learning English words, sentence patterns, and reading in a passive manner. They are not aware of the importance of actively exploring knowledge and resolving their own language difficulties. Students' enthusiasm for class participation is low, resulting in alienation between teachers and students, making the ecological normalization of “teacher-led and student-centered” public English classrooms difficult. An algorithm is used to recognize emotions. The platform is built with the Struts2+Spring + JPA framework to solve the complexity of emotion analysis application development, achieve the effect of loose coupling between layers, and improve the system's scalability and maintainability. The overall architecture of the system is shown in Figure3.Figure 3
Emotion recognition system architecture.The security control layer, open API layer, presentation layer, business logic layer, and data access layer make up the architecture of the emotion recognition algorithm system.(1)
Security control layer: this layer is primarily responsible for maintaining the overall system's security, verifying the access rights of accessed resources, and determining whether they have access rights.(2)
Open API: allows other applications to use the platform's functions by opening the website's functions and interface (API).(3)
Presentation layer: this layer is primarily made up of struts' action and JSP pages. It responds to the user's request by processing it and returning the results to the client.(4)
Business logic layer: this layer is responsible for processing business logic.(5)
Data access layer: data persistence operation using Java's persistence API : JPA. Hibernate's JPA is used to establish the relationship mapping between business entity classes and database tables in the domain model.In natural language processing, the emotion recognition algorithm is crucial. Without a good emotion recognition algorithm model, it is difficult to judge children's emotional tendencies for emotion analysis. The first layer uses an 8 ∗ 8 emotional analysis convolution size, a 4 ∗ 4 pool size, and a pool step size of 2 for the model's parameters. In the experiment, experience shows that using the abovementioned parameter emotion analysis convolution layer as the first layer is best for the 48 ∗ 48 emotion analysis database. Then, in the next layer, gradually reduce the size of the emotion analysis convolution.


## 4. Application of the Emotion Recognition Algorithm in Public English Ecological Classroom

### 4.1. Public English Ecological Classroom Based on the Emotion Recognition Algorithm

The emotion recognition algorithm is used to register. Fill in the application name, description, type, and platform of public English ecological classroom under the background of “Internet +” to create your own application after your registration has been approved. The created application can be audited in the background by the administrator. It is necessary to guide students in breaking the existing balance, forming contradictions and gaps, and promoting process symbiosis in dynamic balance in the process of identifying new situations, completing new tasks, and solving new problems according to the existing balance of cognition and ability when implementing the emotional analysis teaching of the public English ecological classroom reconstruction strategy. The presentation form of teaching content, the construction and application of teaching resources, and whether learning links and methods are selective are all important manifestations of respecting the development of life in the process of reconstruction strategy emotional analysis teaching in the public English ecological classroom. The public English ecology classroom advocates the application of ecological principles, that is, the science of applying the principles and mechanisms of ecosystem, ecological balance, and coevolution to study various educational phenomena and reveal the basic laws and ecological mechanisms of education. Ecological environment and ecological subject-human are two ecological factors in educational ecology. A pair of API keys and API keys from the platform are obtained once the audit is passed. Then, this information can be used to call the platform's API. Indicators of evaluation: the general evaluation indicators in the field of emotion analysis proposed in Chapter 3 are still used in the test experiment of the emotion recognition algorithm: accuracy rate (P), recall rate (R), and F-measure.Experiment 1: only the forward maximum matching method or the reverse maximum matching method is used to operate the same public English ecological classroom in the context of Chinese and English “Internet +.” The outcomes are depicted in Figure4.The results show that when using forward and reverse maximum matching algorithms alone to segment Chinese-English mixed text, the segmentation effect is not ideal, and the accuracy and recall rate are low. The Google similar distance method has a high level of accuracy. The PMI-accuracy ir's is low because it extracts affective analysis tendency phrases based on English grammatical structure, which is not appropriate for Chinese.Experiment 2: using an emotion recognition algorithm to reconstruct a public English ecological classroom with a Chinese and English “Internet +” background. The outcomes are depicted in Figure5.Figure 4
Indicators of the word segmentation algorithm in public English ecological classrooms.Figure 5
Indicators of the emotion recognition algorithm in public English ecological classrooms.The test results show that after continuous judgement and segmentation of Chinese and English mixed fields in the Chinese and English Public English ecological classroom in the text, the Chinese and English Public English ecological classroom in the text has achieved a good reconstruction strategy, and the emotion recognition algorithm has improved in all aspects. The theory of educational ecology and the basic theory of Bloom's teaching classification were used to rebuild the public English ecological classroom. Determining higher vocational English teaching objectives, focusing on sensitive factors in higher vocational English teaching, and optimizing public English teaching design are some of the practical ideas of public English ecological classroom reconstruction. In conclusion, the accuracy and recall rate of the emotion recognition algorithm are higher than those of the forward maximum matching algorithm in the reconstruction strategy of public English ecological classrooms against the backdrop of Chinese and English “Internet plus,” and the effect of the emotion recognition algorithm is the best. The accuracy rate P (precision rate), recall rate R (recall rate), and F-measure evaluation indexes were used in the experiment.The accuracy rate P (precision rate), recall rateR (recall rate), and F-measure were the evaluation indexes used in the experiment.(1)P=AA+B,R=AA+C.All classification texts entered in the classifier are represented by A + B. The numbers A and B represent the number of correctly classified texts, the number of incorrectly classified texts, and the number of incorrectly classified and unclassified texts, respectively. That is, A + C represents the entire test expectation in the formula. The third evaluation index F value is also introduced in this experiment in order to objectively evaluate the overall performance of the experiment. It is capable of calculating a balanced mean accuracy and recall evaluation. The following formula is used to calculate the F value:(2)F−measure=2×P×RP+R.Consider a training sample of a class, and use a vector quantizer to extract a codebook from it. The class conditional probability of the feature vector moves through each codebook vector as its center under the condition of a given class field; then the following is obtained:(3)fYy|ω∞∑j=1qmjexp−y−vj22,where this represents the proportion of training samples of the class. The classifier is defined as follows by the maximum a posteriori probability criterion:(4)ω∧=argΩmaxω∈Ωpω|y=argmaxω∈ΩfYy|ωpω.Several of them imply that the pattern class set influences the class's prior probability. However, because this hierarchical classifier is prone to error transmission, the system's classification performance will suffer as a result of its inherent cascading errors. The model results in a multidimensional space. According to the public English ecological classroom's reconstruction strategy, the global energy spectrum and its principal components both define the spectrum's projection in this multidimensional space, in which the public English ecological classroom sharing the same scene category will be very close to this space, forming the basis of semantic category division. They claim that this feature dimension can be reliably estimated using spectrum and rough location information. For the reconstruction strategy of the public English ecological classroom, each semantic attribute is derived by estimating the global spectral features of public English.(5)s∧=vTd=∑i=1NGvidi=∬Afx,fy2DSTfx,fydfxdfy.The amplitude spectrum of the public English ecological classroom's reconstruction strategy is one of them. The point product of the amplitude spectrum of the public English ecological classroom reconstruction strategy and the template lifetime can be used to estimate each semantic attribute, as shown by the previously mentioned equation.(6)DSTfx,fy=∑i=1NGdiψifx,fy.A DST, called a discriminant template, describes the contribution of each spectral component to the spatial envelope attribute, and it can be parameterized by the column vector d = di determined by learning. NG represents the number of basis functions used for approximation to determine the dimension of each representation.Whenψifx,fy represents the basis of the energy spectrum, it satisfies the orthogonality i≠j, and when Evivj=0, the correlation v=vii=1,...NG of the decomposition coefficient. EE provides nonlocalized structural information, including a low-resolution description of the image energy spectrum. Due to the use of dimension reduction, this representation has low resolution in both spectral and spatial domains.The variablew is obtained by minimizing the objective function as follows:(7)W=argminIX;Y−IW;Y,where I (x; y) is the mutual information between X and y, and I (x; y) is the mutual information between W and y.For discrete random variablesX and y, mutual information I (x; y) is defined as follows:(8)IX;Y=∑x∈X∑y∈Ypx,ylogPx,ypxpy.In which,p(x, y) is the joint probability of x and y, and their marginal probability are p(x) and p(y), respectively. Mutual information I(X; Y) is used to measure the dependence between two variables, that is, how much information of variable X is contained in variable Y.The probability that each eigenvector (Xi, I = 1,..., NYI) belongs to each (gj, j = 1,..., t) and is given as follows:(9)Pgj|Xi,θj=πjnpXi|θjn∑k=1TπknpXi|θkn.Update model parameterθjn=μj,Σj, and the following is obtained:(10)πjn+1=1M∑i=1MpXi,θin,μjn+1=∑i=1Tpgj|Xi,θjnXi∑i=1Tpgj|Xi,θjn.Here, the minimum description length criterion is adopted to select the optimalT value, that is, to maximize the obtained value.(11)log∏k=1Nyi∑i=1TπiNXk;ui;Σi−mT2logNyi.Among them,mT is the number of free parameters with mixed model, which is generally taken as mt.(12)mT=T−1+T⋅d+T⋅dd+12.The parameters learned for all scene categories in public English ecological classroom are connected in series after semantic modelling for each scene category using emotion recognition algorithm model, forming the scene semantic concept space of the entire reconstruction strategy. Although this space may contain semantic redundancy, it has no bearing on subsequent operations, so it will not be optimised here.


### 4.2. Experimental Result

Because some sentences do not have specific emotional words but express an emotion through implied meaning, the number of judgement results is lower than the number of manual markers. As a result, such sentences do not judge their specific emotional tendencies, as shown in Figure6.Figure 6
Experimental results of the emotion recognition algorithm.The success of public English reform hinges on the redesign of public English ecological classrooms against the backdrop of “Internet plus.” Finding the relationship between various factors that affect the development quality of sensitive factors and sensitive factors, as well as reforming and innovating their teaching contents, methods, and means, is required for the optimization of public English teaching design. To improve the design of teaching emotion analysis, we should choose teaching content and forms based on the basic requirements of personnel training and the personality characteristics of students, and innovate teaching methods and means using “survival” as the magic weapon. These comment sets are compared to individual how nets, the Google similarity distance method, pmi-ir, and SVM to demonstrate the effectiveness of this algorithm. HowNet, the emotion recognition algorithm, extracts Se nunigraj from the sentence Li. The similarity value between senuni gramj and HowNet of benchmark word set is calculated using the benchmark word sets extracted in this paper, and the comments are classified by comparing the similarity value. Figure7 depicts the outcome of the judgement.Figure 7
Classification of emotion analysis of four methods.The number of correct negative class judgement results is very low when net judgement is used alone, as can be seen in the graph. The Google similar distance method has a high level of accuracy. The accuracy of pmi-ir is low because it extracts affective analysis tendency phrases based on English grammatical structure, which is incompatible with Chinese; the accuracy of SVM is also high, but SVM requires a large corpus, which is more difficult than unsupervised learning.In this paper, by comparing five methods, the accuracy, recall, and F value of each method are obtained, as shown in Figure8, respectively.Figure 8
Comparison chart of experimental evaluation. (a) Accuracy. (b) Recall rate. (c)F value.
(a)(b)(c)Figures (a) and (b) show that some methods have high accuracy but low recall rates, while others have a high recall rate but low accuracy rate. As a result, the F value is used to compare the effectiveness of each method with the comprehensive accuracy and recall rate. As can be seen in (c), the F values of HowNet and pmi-ir are low, while the F values of Google similarity distance and SVM are relatively high; however, SVM requires a large number of corpora and is troublesome.


## 4.1. Public English Ecological Classroom Based on the Emotion Recognition Algorithm

The emotion recognition algorithm is used to register. Fill in the application name, description, type, and platform of public English ecological classroom under the background of “Internet +” to create your own application after your registration has been approved. The created application can be audited in the background by the administrator. It is necessary to guide students in breaking the existing balance, forming contradictions and gaps, and promoting process symbiosis in dynamic balance in the process of identifying new situations, completing new tasks, and solving new problems according to the existing balance of cognition and ability when implementing the emotional analysis teaching of the public English ecological classroom reconstruction strategy. The presentation form of teaching content, the construction and application of teaching resources, and whether learning links and methods are selective are all important manifestations of respecting the development of life in the process of reconstruction strategy emotional analysis teaching in the public English ecological classroom. The public English ecology classroom advocates the application of ecological principles, that is, the science of applying the principles and mechanisms of ecosystem, ecological balance, and coevolution to study various educational phenomena and reveal the basic laws and ecological mechanisms of education. Ecological environment and ecological subject-human are two ecological factors in educational ecology. A pair of API keys and API keys from the platform are obtained once the audit is passed. Then, this information can be used to call the platform's API. Indicators of evaluation: the general evaluation indicators in the field of emotion analysis proposed in Chapter 3 are still used in the test experiment of the emotion recognition algorithm: accuracy rate (P), recall rate (R), and F-measure.Experiment 1: only the forward maximum matching method or the reverse maximum matching method is used to operate the same public English ecological classroom in the context of Chinese and English “Internet +.” The outcomes are depicted in Figure4.The results show that when using forward and reverse maximum matching algorithms alone to segment Chinese-English mixed text, the segmentation effect is not ideal, and the accuracy and recall rate are low. The Google similar distance method has a high level of accuracy. The PMI-accuracy ir's is low because it extracts affective analysis tendency phrases based on English grammatical structure, which is not appropriate for Chinese.Experiment 2: using an emotion recognition algorithm to reconstruct a public English ecological classroom with a Chinese and English “Internet +” background. The outcomes are depicted in Figure5.Figure 4
Indicators of the word segmentation algorithm in public English ecological classrooms.Figure 5
Indicators of the emotion recognition algorithm in public English ecological classrooms.The test results show that after continuous judgement and segmentation of Chinese and English mixed fields in the Chinese and English Public English ecological classroom in the text, the Chinese and English Public English ecological classroom in the text has achieved a good reconstruction strategy, and the emotion recognition algorithm has improved in all aspects. The theory of educational ecology and the basic theory of Bloom's teaching classification were used to rebuild the public English ecological classroom. Determining higher vocational English teaching objectives, focusing on sensitive factors in higher vocational English teaching, and optimizing public English teaching design are some of the practical ideas of public English ecological classroom reconstruction. In conclusion, the accuracy and recall rate of the emotion recognition algorithm are higher than those of the forward maximum matching algorithm in the reconstruction strategy of public English ecological classrooms against the backdrop of Chinese and English “Internet plus,” and the effect of the emotion recognition algorithm is the best. The accuracy rate P (precision rate), recall rate R (recall rate), and F-measure evaluation indexes were used in the experiment.The accuracy rate P (precision rate), recall rateR (recall rate), and F-measure were the evaluation indexes used in the experiment.(1)P=AA+B,R=AA+C.All classification texts entered in the classifier are represented by A + B. The numbers A and B represent the number of correctly classified texts, the number of incorrectly classified texts, and the number of incorrectly classified and unclassified texts, respectively. That is, A + C represents the entire test expectation in the formula. The third evaluation index F value is also introduced in this experiment in order to objectively evaluate the overall performance of the experiment. It is capable of calculating a balanced mean accuracy and recall evaluation. The following formula is used to calculate the F value:(2)F−measure=2×P×RP+R.Consider a training sample of a class, and use a vector quantizer to extract a codebook from it. The class conditional probability of the feature vector moves through each codebook vector as its center under the condition of a given class field; then the following is obtained:(3)fYy|ω∞∑j=1qmjexp−y−vj22,where this represents the proportion of training samples of the class. The classifier is defined as follows by the maximum a posteriori probability criterion:(4)ω∧=argΩmaxω∈Ωpω|y=argmaxω∈ΩfYy|ωpω.Several of them imply that the pattern class set influences the class's prior probability. However, because this hierarchical classifier is prone to error transmission, the system's classification performance will suffer as a result of its inherent cascading errors. The model results in a multidimensional space. According to the public English ecological classroom's reconstruction strategy, the global energy spectrum and its principal components both define the spectrum's projection in this multidimensional space, in which the public English ecological classroom sharing the same scene category will be very close to this space, forming the basis of semantic category division. They claim that this feature dimension can be reliably estimated using spectrum and rough location information. For the reconstruction strategy of the public English ecological classroom, each semantic attribute is derived by estimating the global spectral features of public English.(5)s∧=vTd=∑i=1NGvidi=∬Afx,fy2DSTfx,fydfxdfy.The amplitude spectrum of the public English ecological classroom's reconstruction strategy is one of them. The point product of the amplitude spectrum of the public English ecological classroom reconstruction strategy and the template lifetime can be used to estimate each semantic attribute, as shown by the previously mentioned equation.(6)DSTfx,fy=∑i=1NGdiψifx,fy.A DST, called a discriminant template, describes the contribution of each spectral component to the spatial envelope attribute, and it can be parameterized by the column vector d = di determined by learning. NG represents the number of basis functions used for approximation to determine the dimension of each representation.Whenψifx,fy represents the basis of the energy spectrum, it satisfies the orthogonality i≠j, and when Evivj=0, the correlation v=vii=1,...NG of the decomposition coefficient. EE provides nonlocalized structural information, including a low-resolution description of the image energy spectrum. Due to the use of dimension reduction, this representation has low resolution in both spectral and spatial domains.The variablew is obtained by minimizing the objective function as follows:(7)W=argminIX;Y−IW;Y,where I (x; y) is the mutual information between X and y, and I (x; y) is the mutual information between W and y.For discrete random variablesX and y, mutual information I (x; y) is defined as follows:(8)IX;Y=∑x∈X∑y∈Ypx,ylogPx,ypxpy.In which,p(x, y) is the joint probability of x and y, and their marginal probability are p(x) and p(y), respectively. Mutual information I(X; Y) is used to measure the dependence between two variables, that is, how much information of variable X is contained in variable Y.The probability that each eigenvector (Xi, I = 1,..., NYI) belongs to each (gj, j = 1,..., t) and is given as follows:(9)Pgj|Xi,θj=πjnpXi|θjn∑k=1TπknpXi|θkn.Update model parameterθjn=μj,Σj, and the following is obtained:(10)πjn+1=1M∑i=1MpXi,θin,μjn+1=∑i=1Tpgj|Xi,θjnXi∑i=1Tpgj|Xi,θjn.Here, the minimum description length criterion is adopted to select the optimalT value, that is, to maximize the obtained value.(11)log∏k=1Nyi∑i=1TπiNXk;ui;Σi−mT2logNyi.Among them,mT is the number of free parameters with mixed model, which is generally taken as mt.(12)mT=T−1+T⋅d+T⋅dd+12.The parameters learned for all scene categories in public English ecological classroom are connected in series after semantic modelling for each scene category using emotion recognition algorithm model, forming the scene semantic concept space of the entire reconstruction strategy. Although this space may contain semantic redundancy, it has no bearing on subsequent operations, so it will not be optimised here.


## 4.2. Experimental Result

Because some sentences do not have specific emotional words but express an emotion through implied meaning, the number of judgement results is lower than the number of manual markers. As a result, such sentences do not judge their specific emotional tendencies, as shown in Figure6.Figure 6
Experimental results of the emotion recognition algorithm.The success of public English reform hinges on the redesign of public English ecological classrooms against the backdrop of “Internet plus.” Finding the relationship between various factors that affect the development quality of sensitive factors and sensitive factors, as well as reforming and innovating their teaching contents, methods, and means, is required for the optimization of public English teaching design. To improve the design of teaching emotion analysis, we should choose teaching content and forms based on the basic requirements of personnel training and the personality characteristics of students, and innovate teaching methods and means using “survival” as the magic weapon. These comment sets are compared to individual how nets, the Google similarity distance method, pmi-ir, and SVM to demonstrate the effectiveness of this algorithm. HowNet, the emotion recognition algorithm, extracts Se nunigraj from the sentence Li. The similarity value between senuni gramj and HowNet of benchmark word set is calculated using the benchmark word sets extracted in this paper, and the comments are classified by comparing the similarity value. Figure7 depicts the outcome of the judgement.Figure 7
Classification of emotion analysis of four methods.The number of correct negative class judgement results is very low when net judgement is used alone, as can be seen in the graph. The Google similar distance method has a high level of accuracy. The accuracy of pmi-ir is low because it extracts affective analysis tendency phrases based on English grammatical structure, which is incompatible with Chinese; the accuracy of SVM is also high, but SVM requires a large corpus, which is more difficult than unsupervised learning.In this paper, by comparing five methods, the accuracy, recall, and F value of each method are obtained, as shown in Figure8, respectively.Figure 8
Comparison chart of experimental evaluation. (a) Accuracy. (b) Recall rate. (c)F value.
(a)(b)(c)Figures (a) and (b) show that some methods have high accuracy but low recall rates, while others have a high recall rate but low accuracy rate. As a result, the F value is used to compare the effectiveness of each method with the comprehensive accuracy and recall rate. As can be seen in (c), the F values of HowNet and pmi-ir are low, while the F values of Google similarity distance and SVM are relatively high; however, SVM requires a large number of corpora and is troublesome.


## 5. Conclusions

To summarise, in the context of the “Internet plus” reconstruction strategy for public English ecological classrooms, “emotional analysis” is an important factor that can have a significant impact on teaching effectiveness, and it must be given sufficient attention. The English teaching ecology will continue to change as Internet technology advances. New English application scenarios, such as virtual reality situational teaching and 5G Internet of Things human-computer interaction, will, for example, have new effects on the English classroom ecology. Teachers should strictly follow the objective development law of students in the process of emotion analysis by using the emotion recognition algorithm while also fully relying on students' actual adaptability in order to avoid the emotional analysis function of “helping the young ones out” and substantially promote the fundamental improvement of the quality and efficiency of emotional analysis. The system's major issues are thoroughly investigated, and an emotion recognition algorithm is proposed. The demand analysis and overall design of the system, the analysis and design of each module of the system, the function realisation, and the use process of the system are all improved on this basis and given. It provides a strong guarantee for the reconstruction of an effective public English ecological classroom by reforming teaching content, teaching resources, teaching methods, and teaching evaluation.



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