# Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big Data

Authors: Yixin Zhao; Ruomu Miao
Journal: Security and Communication Networks
 (2022)

---

## Abstract

The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.


---

## Body

## 1. Introduction

Chen She Shi Jia [the biography of two peasant uprise leaders in the Qin dynasty (221 BC–207 BC)] mentioned, “Without digital communication, public opinion on major events could only be spread by word-of-mouth in ancient China.” Even in modern traditional media, the speed of public opinion generation and dissemination seems to be expected and mostly under control [1–3]. Such a situation has been fundamentally changed since the twenty-first century with the rapid development of digital technology, the Internet, and big data technology, which has given birth to various new media. The emergence of new media has had a significant impact on the development of Network Public Opinion (NPO) [4, 5]. A circle of friends and a message Microblog might easily trigger NPO. Meanwhile, the speed of NPO dissemination over the Internet is unexpected and hard to control. With the increase of Internet voice paths, everyone can be the disseminator and producer of network media. Various online media and NPO can find similar voices on the Internet. A massive amount of information can be excavated from public opinion [6, 7]. Therefore, relevant departments are under great pressure to control and guide NPO. In particular, the public opinion monitoring system can control and guide NPO from the root to avoid possible degeneration or fermentation [8, 9].Hubert et al. [10] understood citizens’ political preferences through emotional analysis of Twitter text data and predicted the popularity of Italian and French leaders and the results of Internet elections in 2011 and 2012. The results proved the correlation between social media and the results of traditional public surveys. Based on this, the future development of some public events was predictable [10]. Li and Xu [11] analyzed the “1.16 school bus accident” through big data technology, used the Internet to collect NPO, and designed the network communication path map of the event [11]. Li et al. [12] analyzed the source of the development of Twitter NPO. They provided a way out of social media network data for NPO research and analysis [12]. Kostka [13] created a big data NPO analysis system, improved the information database into a distributed storage database, and proved that the system was effective [13]. Khalil et al. [14] used Twitter and web crawlers to obtain Hemagglutinin 1 Neuraminidase 1 (H1N1) influenza development data. They tracked the situation of H1N1 influenza [14]. Zaidi et al. [15] studied the Attribute-Based Access Control (ABAC) of web crawlers to facilitate unrestricted Microblog text data access [15]. Dang et al. [16] evaluated the network hot spots by cleaning the text of the NPO from the network, combining it with the domain dictionary and similarity calculation [16]. Mahmood and Qasim [17] used the Support Vector Machine (SVM) and Naive Bayes Model (NBM) to study NPO [17]. Huang [18] obtained Microblog and blog data through a web crawler and studied the classification of NPO texts by using the SVM model [18]. Liu et al. [19] analyzed the interaction between social media-based NPO analysis and government management. The outcome corroborated that through Big Data Analytics (BDA) technology, social media-based NPO analysis provided information support and data source for government management. BDA could be used to monitor and control NPO [19].Scholars above have affirmed the important role of BDA in NPO spread and governance by studying NPO from the aspects of data acquisition, information analysis, and technology application. However, the existing method invests too much time, costs, and energy into NPO data information mining. Besides, their NPO prediction accuracy cannot be guaranteed. Deep Learning (DL) can improve the prediction accuracy but might get affected by many model parameters. To this end, after clarifying the research of BDA on NPO and considering NPO information acquisition, this work creates an improved DL NPO analysis system based on Tensorflow. The method proposed here is of great help to analyzing NPO through the accurate analysis of Microblog text, conducive to judging network media public opinion in the era of big data. It provides a research path for the management and decision-making of network media public opinion. The innovations of this work are as follows: first, given the problems and reasons for the current NPO governance, this work puts forward the improvement path of government NPO governance from the aspects of ideology, system, technology, and structure based on big data and the Internet of Things (IoT). The second is the innovation of research methods. With the help of the improved Long Short-Term Memory (LSTM) model, an NPO analysis system based on Tensorflow is established to explore new network media public opinion management paths.Section1 reviews the development and current situation of NPO and discusses the research on NPO governance by combining big data, and other information technology means in and outside China. The analysis provides the research background for this work. Section 2, the research theory and method part, explains the propagation law of NPO, the application of Natural Language Processing (NLP) technology in NPO analysis, and the DL model, and establishes the NPO analysis system based on the LSTM optimization model. Then, Section 3 is the configuration of the model performance experiment environment. Section 4, the result analysis part analyzes the performance of the LSTM-based NPO analysis system and the classification results of Microblog text under the improved LSTM model. It also describes the construction of public opinion in online media and the path for the government to use big data to improve the ability to govern NPO and help social governance. Lastly, Section 5 is the conclusion, describing the research results, significance, deficiencies, and future research directions.


## 2. Research Theory and Method

### 2.1. Propagation Law of NPO under Network Media

In the context of the Internet, netizens express their understanding and views on various political and social phenomena online. In recent years, they have been able to talk freely about various national and social development issues by taking advantage of the Internet’s “everyone to everyone communication.” They can form a consensus quickly, ferment their emotions, induce action, and then affect society. Netizens’ willingness to express and sense of participation continued to rise, and they actively expressed their opinions on major media. On the one hand, the government strengthens network management and suppresses excessive extreme speech. On the other hand, the response speed of NPO has accelerated, and the government has initially formed the NPO monitoring and feedback from the central to the local. The main sources of NPO in network media include website news comments, Bulletin Board System (BBS), Really Simple Syndication (RSS), Oh, I Seek You (QICQ), Microsoft Network (MSN), Blog, and Microblog. Figure1 lists the formation process of NPO caused by public events in network media.Figure 1
The formation process of NPO in network media.Figure1 shows the formation of NPO as a “linear process.” The opinion formed in each link is interrelated. Under normal circumstances, the NPO formation is a gradual process. When the stimulus–response mechanism appears, NPO will form an emergency mode. Stimulus is an emergency, and NPO is a reactant. Once an emergency occurs, it will spread rapidly on the Internet, causing a strong public response, and the expression of public opinion will be concentrated and intense. Ross et al. [20] analyzed the propagation law and causes of NPO and found that the hot cross-domain public opinion events on the network detonated rapidly. The empathy effect prompted netizens to reveal similar experiences. Unclosed public events triggered netizens to continue to ask questions, and human intervention intensified NPO propagation. There was a public opinion evacuation effect. The intervention of the government’s authority and the high-level society improved the public opinion guidance effect. As a result, the group polarization effect of the public opinion community intensified. Identifying the authenticity of network information became the knowledge of public opinion. Multiple attributions enhanced the voice of collaborative governance [20]. Table 1 details the propagation law of NPO caused by public events in network media.Table 1
The propagation law of NPO caused by public events in network media.
Propagation lawFeaturesCross-domain hot NPO detonates quicklyExplosive transmissionEmpathy drives netizens to support or expose similar experiencesConcealment of subjectThe open loop incident triggered continuous attention and questioning from netizensThe fuzziness of information sourceThe law of round-the-clock, all media, real-time, and all channels of NPO communication is obviousInteraction of netizensOpinion leaders can sometimes even influence the direction of NPODirectivity of opinionsThe group polarization effect of the community of NPO intensifiesSignificance of impactThe development of modern communication technology has greatly changed the mode and pattern of media communication. The development and maturity of the Global Satellite System have popularized personal electronic information products and improved network information transmission speed. A global mass communication pattern dominated by network media and supplemented by other technologies is taking shape. Figure2 shows the network media’s control relationship between IoT, BDA, and NPO.Figure 2
Control connection between IoT, BDA, and NPO in network media.In Figure2, the internal motivation of NPO formation in network media includes interest demand and psychological force. Of these, the public’s favorable demand is the driving source of NPO formation. The external motivation includes the function of the social environment and the force of NPO space in cyberspace. IoT and BDA are used to control and manage the NPO of network media. NPO evaluation needs to consider various factors and variables comprehensively. According to the above formation process, Table 2 explains the propagation laws and characteristics of NPO and the evaluation indexes of NPO.Table 2
Evaluation index of NPO.
Evaluation indexDetailed indexesPropagation and diffusionChanges in NPO information flow and regional distribution of network geographyPublic concernForum, news, blog, and other channels’ NPO information activityContent sensitivityContent sensitivity of NPO informationAttitude tendencyAttitude tendency of NPO information


### 2.2. Application of NLP Technology in NPO Analysis

The DL algorithm has pushed artificial intelligence (AI) to a higher level in recent years. NLP technology has seen broad applications in the network-indexing field. It includes new words mining on the Internet, ranking popular words on the Internet, and analyzing NPO and emotional bias using long and short sentences in the text. It also involves major social media platforms’ topics and retrieval trends. These applications can help enterprises or individuals better understand the topics and events of public interest and count the development trend of topics and events. Ultimately, it helps the society make effective judgments and relevant governance. Accordingly, by researching, processing, and analyzing NPO text big data, this work summarizes the characteristics of text expression under IoT and BDA. Specifically, it solves the technical challenges in NLP-related applications by exploring natural language’s grammar logic and character vector representation. Then, the core technology chooses the deep semantic analysis to realize the application requirements of NPO text intelligent classification, intelligent text recommendation, and automatic generation of text information based on semantic understanding. The aim is to promote the social NPO governance from digitization and networking to intelligent development. Figure3 depicts the application principle of NLP technology.Figure 3
Application principle of NLP technology.According to Figure3, NLP works through machine learning (ML). ML systems store words and how they are combined like any other form of data. Phrases, sentences, and sometimes the book’s contents are input into the ML engine, where grammatical rules, people’s real language habits, or both are used for processing. Then, the computer uses the data to find the pattern and infer the next result. Notably, while Machine translation is a powerful NLP application, searching is one of the most common uses. Every time people search for content in Google or Bing search engine, they manually input data into the system. Upon clicking on a search result, the search engine interprets and confirms that it has found the correct result and uses this information to search better in the future. DL technology analyzes the semantic information of public opinion information and intelligently and accurately classifies the information. Then, it recommends relevant results and automatically generates concise main content of public opinion. The data acquisition module collects NPO texts in real-time and saves the text information to the database of the data storage layer. Simultaneously, the data-preprocessing module cleans and organizes the data according to the information processing needs. It classifies, summarizes, recommends texts, and extracts keywords through the NLP engine of the online computing module. Finally, the model results are saved to the database [21]. Further, NLP technology processes the Microblog texts. Figure 4 illustrates the application principle of NLP technology in NPO information collection.Figure 4
Application principle of NLP technology in NPO information collection.Figure4 first collects the NPO upon public event occurrence from major media websites as the learning samples of ML. Second, it inputs the sample features into the ML model to generate the NPO paragraph recognition model. Finally, the extracted features of the target NPO document are input into the ML model.


### 2.3. Common DL Models

Hochreiter and Schmidhuber proposed LSTM in their academic paper Long Short-Term Memory to solve the gradient disappearance in common DL models. The original LSTM has only an input gate and output gate. So far, a forget gate is also introduced into LSTM, an improved version of Gers in his bookLearning to Forget: Continual Prediction with LSTM. Figure 5 draws an LSTM structure.Figure 5
LSTM structure.In Figure5, the LSTM structure includes three gate structures. Essentially, they are the weights ∈ [0, 1] and can be realized by the Sigmoid function. The cell represents the state of the current memory block and corresponds to the hidden layer neurons in the original Recurrent Neural Network (RNN). The white circles and functions represent the activation function (AF). There is a general standard for selecting AF. In particular, f is the AF of the gate structure. g denotes the AF of the cell input, and h indicates the AF of the cell output. Generally, the input gate, output gate, and forget gate choose the Sigmoid function as AF. The input and cell select the tanh function for activation [22]. The specific calculation of the input gate, forget gate, and output gate are given in equations (1)–(3).(1)it=sigmoidWi⋅ht−1,xt+bi,(2)ft=sigmoidWf⋅ht−1,xt+bf,(3)ot=sigmoidWo⋅ht−1,xt+bo.Here,it, ft, and ot are the output of the input gate, forget gate, and output gate, respectively. ht−1 represents the output of the previous moment. xt is the input at the current moment. These two parameters enter the forget gate first to get the information with a smaller weight and then determine the information to be discarded: ft. The output gate updates information as it that has a larger weight than the previous layer information. W and b mean the weight and threshold in each gate, respectively. Then, the forget gate discards the information through (2). Afterward, new information is added to the cell state. First, the information will be updated using the input gate, and then the new candidate cell information C˜t will be obtained through a tanh layer. The new candidate information may be updated to the new cell. The expression of candidate cell status reads:(4)C˜t=tanhWC⋅ht−1,xt+bC.Next, the old cell informationCt−1 is updated and converted into new cell information Ct. Here is the update rule: the forget gate determines what to discard in the old cell information. Then, the input gate calculates the candidate cell information C˜ and adds it to get the new cell information Ct. The specific expression reads:(5)Ct=ft⋅Ct−1+it⋅C˜t.Finally,ht−1 and xt are inputted to judge the state characteristics of the output cell. Then, the input will pass through the Sigmoid layer in the output gate to determine the judgment conditions. As a result, a vector ∈ [−1, 1] is obtained through the tanh layer. Further, the vector is multiplied by the judgment conditions of the output gate to obtain the final output. The specific calculation reads:(6)ht=ot⋅tanhCt.Additionally, the multilayer LSTM parameters are calculated by (7):(7)n×4dhdh+dx+dh.In (7), n represents the number of layers of LSTM. dx and dh are the input and output dimensions. The LSTM has three gates, which are more complex than the direct calculation output. For experimental comparison, this work employs Gated Recurrent Unit (GRU). Figure 6 presents the GRU structure.Figure 6
GRU structure.In Figure6, R and Z are reset gate and update gate, respectively, and H is the update output of Z. Comparing Figures 5 and 6 reveal that GRU and LSTM have some similarities. Their differences are that GRU does not have the cell in LSTM but directly calculates the output. The update gate in GRU is similar to the fusion of the input gate and forget gate in LSTM. Observing the structure and the gate connected at the last moment shows that the forget gate in LSTM is actually divided into the update gate and reset gate in GRU [23]. The calculation process of GRU is detailed in equations (8) to (11):(8)Rt=sigmoidWr⋅ht−1,xt+br,(9)Zt=sigmoidWz⋅ht−1,xt+bz,(10)H˜t=tanhW⋅xt,ht−1⊙Rt,(11)Ht=1−Zt⊙Ht−1+Zt⊙H˜t.Here,ht−1 and xt are the output and input of the previous moment, respectively. The output of the current moment is Ht. Then, weighted-multiplication is performed on ht−1 and xt and is activated by sigmoid to obtain the update gate Zt and the reset gate Rt. The reset gate controls the previous state information to be written to the current candidate set H˜t. W and b represent the weight and threshold in each information update, respectively.


### 2.4. Network Media NPO Analysis System Based on LSTM Neural Network (NN) Optimization Model

According to Section2.3, the NN model supported by IoT and BDA is preliminarily analyzed. The commonly used DL frameworks TensoFflow and Pytorch also have some subtle differences in the implementation of LSTM. TensorFlow is the second-generation AI learning system developed by Google based on DistBelief. TensorFlow can be applied in many places, such as Speech Recognition (SR), Natural Language Understanding (NLU), and Computer Vision (CV). It can run on everything from smartphones to thousands of data center servers. However, there are still some problems in implementing LSTM on TensorFlow.In practical application, the traditional computing platform cannot carry such a large amount of data computing of LSTM. Due to the massive amount of parameters and massive training data and reasoning test data, the training model and model reasoning have high-computational complexity. Thus, the power consumption is generated from the computing platform. Some researchers introduce the delta algorithm to accelerate hardware calculation of sparse LSTM model. They used the numerical similarity of sequence data to construct and mine the sparsity of sequence data, reconstruct the LSTM model, and accelerate the algorithm. This method has obvious limitations in the scope of application. The hardware acceleration of LSTM model reconstruction based on the delta is complex and not conducive to hardware deployment. In this work, the sparsity of LSTM model parameter weights is improved. The specific improvement process is shown in Figure7.Figure 7
Improvement process of parameter weight sparsity of the LSTM model.Figure7 prunes the LSTM model by removing redundant parameters or setting them to 0. Since model training is based on floating-point numbers and consumes resources, K-Means Clustering (KMC) can be used to minimize parameters [24]. (9) reveals the specific calculation:(12)argmin∑i=1k∑w∈ciw−ci2.In (9), w is the training parameter set. ci denotes the parameter after classification. The weight data after pruning and quantization are greatly reduced for the point multiplication operation in LSTM. In order to transmit only effective weights, the weight data needs to be sparsely encoded. The trained network weights are trimmed into the sparse matrix, and then the nonzero weights are fine-tuned by the Fine-grained Structured Sparsity (FGSS) method. After the network weight is compressed, the data space and bandwidth are reduced by half. The sparse tensor core of the original network weight doubles the throughput of mathematical calculation by skipping zero. Against gradient disappearance, the residual connection is used to add 1 to the error reciprocal in the training process of the LSTM model. At this time, even if the original error reciprocal is tiny, the error can still be effectively back propagated to optimize the LSTM model. The proposed network media NPO analysis system based on LSTM using TensorFlow is portrayed in Figure 8.Figure 8
Network media NPO analysis system based on LSTM NN implemented on tensorflow.In Figure8, the LSTM NPO analysis system based on Tensorflow first inputs the search keywords. It obtains the event NPO information in the web crawler and stores the data in the database. Second, Tensorflow processes the data and uses the optimized LSTM model to analyze public opinion. In order to solve the long-term dependence problem in the LSTM, the residual connection is introduced. It combines the original word vector information with the extraction result of the model and is inputted to the attention layer. Finally, the classification result in the output layer is obtained.


## 2.1. Propagation Law of NPO under Network Media

In the context of the Internet, netizens express their understanding and views on various political and social phenomena online. In recent years, they have been able to talk freely about various national and social development issues by taking advantage of the Internet’s “everyone to everyone communication.” They can form a consensus quickly, ferment their emotions, induce action, and then affect society. Netizens’ willingness to express and sense of participation continued to rise, and they actively expressed their opinions on major media. On the one hand, the government strengthens network management and suppresses excessive extreme speech. On the other hand, the response speed of NPO has accelerated, and the government has initially formed the NPO monitoring and feedback from the central to the local. The main sources of NPO in network media include website news comments, Bulletin Board System (BBS), Really Simple Syndication (RSS), Oh, I Seek You (QICQ), Microsoft Network (MSN), Blog, and Microblog. Figure1 lists the formation process of NPO caused by public events in network media.Figure 1
The formation process of NPO in network media.Figure1 shows the formation of NPO as a “linear process.” The opinion formed in each link is interrelated. Under normal circumstances, the NPO formation is a gradual process. When the stimulus–response mechanism appears, NPO will form an emergency mode. Stimulus is an emergency, and NPO is a reactant. Once an emergency occurs, it will spread rapidly on the Internet, causing a strong public response, and the expression of public opinion will be concentrated and intense. Ross et al. [20] analyzed the propagation law and causes of NPO and found that the hot cross-domain public opinion events on the network detonated rapidly. The empathy effect prompted netizens to reveal similar experiences. Unclosed public events triggered netizens to continue to ask questions, and human intervention intensified NPO propagation. There was a public opinion evacuation effect. The intervention of the government’s authority and the high-level society improved the public opinion guidance effect. As a result, the group polarization effect of the public opinion community intensified. Identifying the authenticity of network information became the knowledge of public opinion. Multiple attributions enhanced the voice of collaborative governance [20]. Table 1 details the propagation law of NPO caused by public events in network media.Table 1
The propagation law of NPO caused by public events in network media.
Propagation lawFeaturesCross-domain hot NPO detonates quicklyExplosive transmissionEmpathy drives netizens to support or expose similar experiencesConcealment of subjectThe open loop incident triggered continuous attention and questioning from netizensThe fuzziness of information sourceThe law of round-the-clock, all media, real-time, and all channels of NPO communication is obviousInteraction of netizensOpinion leaders can sometimes even influence the direction of NPODirectivity of opinionsThe group polarization effect of the community of NPO intensifiesSignificance of impactThe development of modern communication technology has greatly changed the mode and pattern of media communication. The development and maturity of the Global Satellite System have popularized personal electronic information products and improved network information transmission speed. A global mass communication pattern dominated by network media and supplemented by other technologies is taking shape. Figure2 shows the network media’s control relationship between IoT, BDA, and NPO.Figure 2
Control connection between IoT, BDA, and NPO in network media.In Figure2, the internal motivation of NPO formation in network media includes interest demand and psychological force. Of these, the public’s favorable demand is the driving source of NPO formation. The external motivation includes the function of the social environment and the force of NPO space in cyberspace. IoT and BDA are used to control and manage the NPO of network media. NPO evaluation needs to consider various factors and variables comprehensively. According to the above formation process, Table 2 explains the propagation laws and characteristics of NPO and the evaluation indexes of NPO.Table 2
Evaluation index of NPO.
Evaluation indexDetailed indexesPropagation and diffusionChanges in NPO information flow and regional distribution of network geographyPublic concernForum, news, blog, and other channels’ NPO information activityContent sensitivityContent sensitivity of NPO informationAttitude tendencyAttitude tendency of NPO information


## 2.2. Application of NLP Technology in NPO Analysis

The DL algorithm has pushed artificial intelligence (AI) to a higher level in recent years. NLP technology has seen broad applications in the network-indexing field. It includes new words mining on the Internet, ranking popular words on the Internet, and analyzing NPO and emotional bias using long and short sentences in the text. It also involves major social media platforms’ topics and retrieval trends. These applications can help enterprises or individuals better understand the topics and events of public interest and count the development trend of topics and events. Ultimately, it helps the society make effective judgments and relevant governance. Accordingly, by researching, processing, and analyzing NPO text big data, this work summarizes the characteristics of text expression under IoT and BDA. Specifically, it solves the technical challenges in NLP-related applications by exploring natural language’s grammar logic and character vector representation. Then, the core technology chooses the deep semantic analysis to realize the application requirements of NPO text intelligent classification, intelligent text recommendation, and automatic generation of text information based on semantic understanding. The aim is to promote the social NPO governance from digitization and networking to intelligent development. Figure3 depicts the application principle of NLP technology.Figure 3
Application principle of NLP technology.According to Figure3, NLP works through machine learning (ML). ML systems store words and how they are combined like any other form of data. Phrases, sentences, and sometimes the book’s contents are input into the ML engine, where grammatical rules, people’s real language habits, or both are used for processing. Then, the computer uses the data to find the pattern and infer the next result. Notably, while Machine translation is a powerful NLP application, searching is one of the most common uses. Every time people search for content in Google or Bing search engine, they manually input data into the system. Upon clicking on a search result, the search engine interprets and confirms that it has found the correct result and uses this information to search better in the future. DL technology analyzes the semantic information of public opinion information and intelligently and accurately classifies the information. Then, it recommends relevant results and automatically generates concise main content of public opinion. The data acquisition module collects NPO texts in real-time and saves the text information to the database of the data storage layer. Simultaneously, the data-preprocessing module cleans and organizes the data according to the information processing needs. It classifies, summarizes, recommends texts, and extracts keywords through the NLP engine of the online computing module. Finally, the model results are saved to the database [21]. Further, NLP technology processes the Microblog texts. Figure 4 illustrates the application principle of NLP technology in NPO information collection.Figure 4
Application principle of NLP technology in NPO information collection.Figure4 first collects the NPO upon public event occurrence from major media websites as the learning samples of ML. Second, it inputs the sample features into the ML model to generate the NPO paragraph recognition model. Finally, the extracted features of the target NPO document are input into the ML model.


## 2.3. Common DL Models

Hochreiter and Schmidhuber proposed LSTM in their academic paper Long Short-Term Memory to solve the gradient disappearance in common DL models. The original LSTM has only an input gate and output gate. So far, a forget gate is also introduced into LSTM, an improved version of Gers in his bookLearning to Forget: Continual Prediction with LSTM. Figure 5 draws an LSTM structure.Figure 5
LSTM structure.In Figure5, the LSTM structure includes three gate structures. Essentially, they are the weights ∈ [0, 1] and can be realized by the Sigmoid function. The cell represents the state of the current memory block and corresponds to the hidden layer neurons in the original Recurrent Neural Network (RNN). The white circles and functions represent the activation function (AF). There is a general standard for selecting AF. In particular, f is the AF of the gate structure. g denotes the AF of the cell input, and h indicates the AF of the cell output. Generally, the input gate, output gate, and forget gate choose the Sigmoid function as AF. The input and cell select the tanh function for activation [22]. The specific calculation of the input gate, forget gate, and output gate are given in equations (1)–(3).(1)it=sigmoidWi⋅ht−1,xt+bi,(2)ft=sigmoidWf⋅ht−1,xt+bf,(3)ot=sigmoidWo⋅ht−1,xt+bo.Here,it, ft, and ot are the output of the input gate, forget gate, and output gate, respectively. ht−1 represents the output of the previous moment. xt is the input at the current moment. These two parameters enter the forget gate first to get the information with a smaller weight and then determine the information to be discarded: ft. The output gate updates information as it that has a larger weight than the previous layer information. W and b mean the weight and threshold in each gate, respectively. Then, the forget gate discards the information through (2). Afterward, new information is added to the cell state. First, the information will be updated using the input gate, and then the new candidate cell information C˜t will be obtained through a tanh layer. The new candidate information may be updated to the new cell. The expression of candidate cell status reads:(4)C˜t=tanhWC⋅ht−1,xt+bC.Next, the old cell informationCt−1 is updated and converted into new cell information Ct. Here is the update rule: the forget gate determines what to discard in the old cell information. Then, the input gate calculates the candidate cell information C˜ and adds it to get the new cell information Ct. The specific expression reads:(5)Ct=ft⋅Ct−1+it⋅C˜t.Finally,ht−1 and xt are inputted to judge the state characteristics of the output cell. Then, the input will pass through the Sigmoid layer in the output gate to determine the judgment conditions. As a result, a vector ∈ [−1, 1] is obtained through the tanh layer. Further, the vector is multiplied by the judgment conditions of the output gate to obtain the final output. The specific calculation reads:(6)ht=ot⋅tanhCt.Additionally, the multilayer LSTM parameters are calculated by (7):(7)n×4dhdh+dx+dh.In (7), n represents the number of layers of LSTM. dx and dh are the input and output dimensions. The LSTM has three gates, which are more complex than the direct calculation output. For experimental comparison, this work employs Gated Recurrent Unit (GRU). Figure 6 presents the GRU structure.Figure 6
GRU structure.In Figure6, R and Z are reset gate and update gate, respectively, and H is the update output of Z. Comparing Figures 5 and 6 reveal that GRU and LSTM have some similarities. Their differences are that GRU does not have the cell in LSTM but directly calculates the output. The update gate in GRU is similar to the fusion of the input gate and forget gate in LSTM. Observing the structure and the gate connected at the last moment shows that the forget gate in LSTM is actually divided into the update gate and reset gate in GRU [23]. The calculation process of GRU is detailed in equations (8) to (11):(8)Rt=sigmoidWr⋅ht−1,xt+br,(9)Zt=sigmoidWz⋅ht−1,xt+bz,(10)H˜t=tanhW⋅xt,ht−1⊙Rt,(11)Ht=1−Zt⊙Ht−1+Zt⊙H˜t.Here,ht−1 and xt are the output and input of the previous moment, respectively. The output of the current moment is Ht. Then, weighted-multiplication is performed on ht−1 and xt and is activated by sigmoid to obtain the update gate Zt and the reset gate Rt. The reset gate controls the previous state information to be written to the current candidate set H˜t. W and b represent the weight and threshold in each information update, respectively.


## 2.4. Network Media NPO Analysis System Based on LSTM Neural Network (NN) Optimization Model

According to Section2.3, the NN model supported by IoT and BDA is preliminarily analyzed. The commonly used DL frameworks TensoFflow and Pytorch also have some subtle differences in the implementation of LSTM. TensorFlow is the second-generation AI learning system developed by Google based on DistBelief. TensorFlow can be applied in many places, such as Speech Recognition (SR), Natural Language Understanding (NLU), and Computer Vision (CV). It can run on everything from smartphones to thousands of data center servers. However, there are still some problems in implementing LSTM on TensorFlow.In practical application, the traditional computing platform cannot carry such a large amount of data computing of LSTM. Due to the massive amount of parameters and massive training data and reasoning test data, the training model and model reasoning have high-computational complexity. Thus, the power consumption is generated from the computing platform. Some researchers introduce the delta algorithm to accelerate hardware calculation of sparse LSTM model. They used the numerical similarity of sequence data to construct and mine the sparsity of sequence data, reconstruct the LSTM model, and accelerate the algorithm. This method has obvious limitations in the scope of application. The hardware acceleration of LSTM model reconstruction based on the delta is complex and not conducive to hardware deployment. In this work, the sparsity of LSTM model parameter weights is improved. The specific improvement process is shown in Figure7.Figure 7
Improvement process of parameter weight sparsity of the LSTM model.Figure7 prunes the LSTM model by removing redundant parameters or setting them to 0. Since model training is based on floating-point numbers and consumes resources, K-Means Clustering (KMC) can be used to minimize parameters [24]. (9) reveals the specific calculation:(12)argmin∑i=1k∑w∈ciw−ci2.In (9), w is the training parameter set. ci denotes the parameter after classification. The weight data after pruning and quantization are greatly reduced for the point multiplication operation in LSTM. In order to transmit only effective weights, the weight data needs to be sparsely encoded. The trained network weights are trimmed into the sparse matrix, and then the nonzero weights are fine-tuned by the Fine-grained Structured Sparsity (FGSS) method. After the network weight is compressed, the data space and bandwidth are reduced by half. The sparse tensor core of the original network weight doubles the throughput of mathematical calculation by skipping zero. Against gradient disappearance, the residual connection is used to add 1 to the error reciprocal in the training process of the LSTM model. At this time, even if the original error reciprocal is tiny, the error can still be effectively back propagated to optimize the LSTM model. The proposed network media NPO analysis system based on LSTM using TensorFlow is portrayed in Figure 8.Figure 8
Network media NPO analysis system based on LSTM NN implemented on tensorflow.In Figure8, the LSTM NPO analysis system based on Tensorflow first inputs the search keywords. It obtains the event NPO information in the web crawler and stores the data in the database. Second, Tensorflow processes the data and uses the optimized LSTM model to analyze public opinion. In order to solve the long-term dependence problem in the LSTM, the residual connection is introduced. It combines the original word vector information with the extraction result of the model and is inputted to the attention layer. Finally, the classification result in the output layer is obtained.


## 3. Experimental Environment Configuration and Experimental Data Processing

This experiment uses Python, a programming language widely used in data processing and DL. The experiment is deployed under the Linux environment. The operating system chooses Ubuntu 18.04 with a 16 G Radom Access Memory (RAM), an Intel i7-9900k Central Processing Unit (CPU), and the GTX2080Ti Graphics Processing Unit (GPU). The DL framework is TensorFlow 2.0, and the Integrated Drive Electronics (IDE) is PyCharm 2020. The experimental data training adopts the Microblog text data set, containing 100,000 Microblog texts and the corresponding emotional tendencies that predecessors have marked. Meaningless characters are removed. Redundant spaces are merged. Microblog expressions are retained. Some texts rendered meaningless after data cleaning will be removed from the data set.


## 4. Results Analysis

### 4.1. Result Analysis of DL Models

Shekhar et al. [25] applied LSTM to social media. LSTM network model can learn and accurately predict the language used in social media texts. The performance of different models was compared, reflecting the proposed model’s advantages [25]. Inspired by this, this section takes the Microblog text content as the primary research subject. Then, the performance is analyzed for the proposed LSTM-based network media NPO analysis system by comparing different DL models: LSTM, GRU, DPCNN, and Text Convolutional Neural Network (TextCNN). Each model’s characteristics are comparatively analyzed to put forward an improved LSTM model. Figure 9 plots the model analysis results. Figures 9(a) and 9(b) present the results of the accuracy and loss rate of network media NPO analysis, respectively.Figure 9
The analysis of the model results: (a) The accuracy of network media NPO analysis. (b) Loss rate of network media NPO analysis.
(a)(b)According to Figure9, the accuracy of the improved LSTM model is significantly higher than that of other models, and the loss rate is significantly lower than that of other models. The average accuracy of the improved LSTM, GRU, DPCNN, and TextCNN is 74.65%, 72.61%, 71.13%, and 71.12%, respectively. Evidently, the prediction accuracy of DPCNN and TextCNN is similar. The average loss rate of the improved LSTM, GRU, DPCNN, and TextCNN is 51.52%, 54.29%, 57.64%, and 56.15%. The prediction accuracy of DPCNN and TextCNN is similar, but the loss is higher than the other two models. To sum up, the accuracy of the improved LSTM model is significantly higher than that of other models. In the ideal case, the accuracy can be improved by nearly two percentage points.


### 4.2. Emotional Analysis and Classification Results of Network Media NPO under the Improved LSTM Model

In this verification, the relevant information about “Lao Tan pickled cabbage bag is actually pickled in a pit” on the Microblog platform in March 2022 is used for data collection. Some texts are extracted for the improved LSTM-based network media NPO’s emotion analysis. “1” indicates a correct classification, and “0” is a wrong classification. Figure10 describes the emotional classification results.Figure 10
Model emotion classification results.In Figure10, the improved LSTM model correctly classifies the emotion of the extracted Microblog text by up to 80.00%, regardless of whether the extracted text fully interprets the keyword. Thus, the improved LSTM model has a good effect on analyzing NPO and emotion in network media.In the Internet era, the smooth, practical, and good implementation of the network Mass Line is a test of the social public service level and social governance ability of the Communist Party of China (CPC) and the government. Applying BDA has made the NPO governance service more accurate and significantly improved the efficiency of social governance. A harmonious and healthy network Mass Line is an important means of contacting the masses, serving the masses, and innovating social governance. Netizens come from ordinary people, and from their Internet surfing behaviors come the NPO. Scientifically and flexibly using the Internet technology to understand NO and carry out work can help provide strong technical support for network information processing and analysis using BDA and AI. It enriches government network platforms’ timely interaction and efficient communication with the citizens. Meanwhile, the government can find the difficulties and blind spots of social governance from many NOP and netizens’ messages to create a new starting point for optimizing social governance. The key to using the Internet to improve social governance is to turn NPO into practical action. On the one hand, there is a need to gradually deepen the construction of the network platform and constantly perfect the official websites, Microblogs, and official accounts of the CPC and government. Simultaneously, the network interaction channels should be unblocked to ensure that NPO can provide an important decision and policy basis for the CPC and the government. On the other hand, it is imperative to explore new ways of government people interaction and actively respond to netizens’ messages and public concerns. This way, the government can effectively offer timely feedback and deadline and address reasonable public demands. Only by asking about the people’s plans can the state better serve the people. Lastly, utilizing the Internet can collect people’s voices and emotions. BDA can help top-level design and policy implementation and continuously upgrade and optimize social governance concepts and capabilities. It is fair to say that people’s happiness will be doubled, and a safer, more substantial, and reliable network environment will be envisioned in the future.


## 4.1. Result Analysis of DL Models

Shekhar et al. [25] applied LSTM to social media. LSTM network model can learn and accurately predict the language used in social media texts. The performance of different models was compared, reflecting the proposed model’s advantages [25]. Inspired by this, this section takes the Microblog text content as the primary research subject. Then, the performance is analyzed for the proposed LSTM-based network media NPO analysis system by comparing different DL models: LSTM, GRU, DPCNN, and Text Convolutional Neural Network (TextCNN). Each model’s characteristics are comparatively analyzed to put forward an improved LSTM model. Figure 9 plots the model analysis results. Figures 9(a) and 9(b) present the results of the accuracy and loss rate of network media NPO analysis, respectively.Figure 9
The analysis of the model results: (a) The accuracy of network media NPO analysis. (b) Loss rate of network media NPO analysis.
(a)(b)According to Figure9, the accuracy of the improved LSTM model is significantly higher than that of other models, and the loss rate is significantly lower than that of other models. The average accuracy of the improved LSTM, GRU, DPCNN, and TextCNN is 74.65%, 72.61%, 71.13%, and 71.12%, respectively. Evidently, the prediction accuracy of DPCNN and TextCNN is similar. The average loss rate of the improved LSTM, GRU, DPCNN, and TextCNN is 51.52%, 54.29%, 57.64%, and 56.15%. The prediction accuracy of DPCNN and TextCNN is similar, but the loss is higher than the other two models. To sum up, the accuracy of the improved LSTM model is significantly higher than that of other models. In the ideal case, the accuracy can be improved by nearly two percentage points.


## 4.2. Emotional Analysis and Classification Results of Network Media NPO under the Improved LSTM Model

In this verification, the relevant information about “Lao Tan pickled cabbage bag is actually pickled in a pit” on the Microblog platform in March 2022 is used for data collection. Some texts are extracted for the improved LSTM-based network media NPO’s emotion analysis. “1” indicates a correct classification, and “0” is a wrong classification. Figure10 describes the emotional classification results.Figure 10
Model emotion classification results.In Figure10, the improved LSTM model correctly classifies the emotion of the extracted Microblog text by up to 80.00%, regardless of whether the extracted text fully interprets the keyword. Thus, the improved LSTM model has a good effect on analyzing NPO and emotion in network media.In the Internet era, the smooth, practical, and good implementation of the network Mass Line is a test of the social public service level and social governance ability of the Communist Party of China (CPC) and the government. Applying BDA has made the NPO governance service more accurate and significantly improved the efficiency of social governance. A harmonious and healthy network Mass Line is an important means of contacting the masses, serving the masses, and innovating social governance. Netizens come from ordinary people, and from their Internet surfing behaviors come the NPO. Scientifically and flexibly using the Internet technology to understand NO and carry out work can help provide strong technical support for network information processing and analysis using BDA and AI. It enriches government network platforms’ timely interaction and efficient communication with the citizens. Meanwhile, the government can find the difficulties and blind spots of social governance from many NOP and netizens’ messages to create a new starting point for optimizing social governance. The key to using the Internet to improve social governance is to turn NPO into practical action. On the one hand, there is a need to gradually deepen the construction of the network platform and constantly perfect the official websites, Microblogs, and official accounts of the CPC and government. Simultaneously, the network interaction channels should be unblocked to ensure that NPO can provide an important decision and policy basis for the CPC and the government. On the other hand, it is imperative to explore new ways of government people interaction and actively respond to netizens’ messages and public concerns. This way, the government can effectively offer timely feedback and deadline and address reasonable public demands. Only by asking about the people’s plans can the state better serve the people. Lastly, utilizing the Internet can collect people’s voices and emotions. BDA can help top-level design and policy implementation and continuously upgrade and optimize social governance concepts and capabilities. It is fair to say that people’s happiness will be doubled, and a safer, more substantial, and reliable network environment will be envisioned in the future.


## 5. Conclusion

Under the background of IoT big data, this work analyzes the public opinion of online media. The performance of the proposed LSTM NPO analysis system based on TensorFlow is tested by comparing it with different DL models. The NPO and emotion classification of Microblog text by the improved LSTM model is verified based on the keyword “Lao Tan pickled cabbage bag is actually pickled in a pit” on the Microblog platform in March 2022. The results show that the average accuracy of the improved LSTM model is 74.65%, and the average loss rate is 51.52%. The improved LSTM model’s accuracy is significantly higher than other models, and the loss is significantly lower. Therefore, the improved LSTM model can classify the emotion of the extracted Microblog text correctly, reaching as high as 80.00% accuracy. In the ideal case, the accuracy can be increased by nearly two percentage points. The results indicate that adding residual connection and attention mechanism can improve the model’s emotional feature extraction ability and emotional discrimination ability in the text. The proposed method is of great help in analyzing NPO through Microblog texts. Lastly, some research pitfalls need future discussion. The sample size is too small to classify NOP and emotion in network media, and later research can expand the sample size. Moreover, with the continuous development of ML, the research on network media NPO also needs to be updated. Besides, since this work provides NPO keywords, researchers need to explore whether NLP technology can automatically extract the keywords of network media NPO in the future.



---

*Source: 2459815-2022-08-17.xml*