# The Basic Principles of Marxism with the Internet as a Carrier

Authors: Wang Ruichen
Journal: Mathematical Problems in Engineering
 (2022)

---

## Abstract

We live in a real society today with the Internet as the carrier. Marxist philosophy is of great significance to the study of computer science and technology majors, such as the standardization of learning methods and the improvement of learning horizons. In order to study the ideology of the current Internet carrier, this study first introduces the ideology in the Internet scenario and again describes the basic principles of Marxism. Web text data were collected from both static and dynamic websites, and the data were initially preprocessed using the ICTCLAS word separation tool. After that, a text lexicon set related to Marxism was built and combined with the surplus value calculation formula. Analysis of the text data shows that the expectation error of the Internet population approximately obeys a positive-term distribution, and the online text data are feasible in the estimation of the influence of Marxism. The statistical results show that the percentage of serious philosophical discussions from 2017 to 2021 in which Marxism is mentioned has steadily increased from 62% to 78%, indicating that Marxism still has great influence to this day. For these three different attitudes toward Marxism, the number of comments agreeing and disagreeing differed significantly, indicating that different groups of people show very different attitudes toward Marxism.


---

## Body

## 1. Introduction

The Internet has not only provided unprecedented convenience for human society but also brought great changes. The Internet is a huge carrier of information, and along with the steady forward development of the global economy, its tentacles have extended to every corner of the Earth [1]. And the websites, news media, social software, and entertainment information built on its technology are profoundly changing human behavior and thinking patterns with astonishing influence [2]. And multiple philosophical schools of thought have lent themselves to the dissemination of articles, social comments, and videos, either long or short, in a different way than before and ideologies are being transmitted from person to person in a whole new way.The existence of society determines the social consciousness, and different social forms are supposed to have different social consciousness. However, influenced by the rapid dissemination of information through the Internet, multiple social consciousnesses have begun to spread in societies where they do not exist [3]. Diverse cultural communication brings opportunities for the survival of relatively small and disadvantaged groups, providing them with more tolerance and care. And in some ideologically homogeneous and narrow-minded societies, the spread of pluralistic ideas is like a seed that takes root in the soil of a group of people’s minds. After some years, it is bound to bring about changes and evolution [4]. And ideology needs to be matched with economic development; matching ideology and economic level can promote national cohesion and lead people to greater victory. And mismatched ideology and economic level can be counterproductive to social development, negatively impacting economic development and people’s working life, and hindering social progress and development [5]. Diversity of thought is not beneficial across the board, and there is a great deal of backwardness and dross in philosophical thought, while the rapid but shallow communication characteristics of the Internet make a large part of the population lose the ability to think deeply. This has facilitated the spread of some extreme contents, and people no longer look into the reasons and meanings behind the events, but simply vent their emotions [6]. Moreover, in the last decade or so, all events that should be taken seriously have been entertained by the dissemination of texts of no more than one hundred words, the insertion of a large number of emoticons, and the prevalence of short videos [7]. The creation of this universal pan-entertainment is inevitably detrimental to the improvement of people’s state of mind and the rise of national economic level.Marxism was proposed by Karl Marx and Friedrich Engels in the mid-to-late 19th century, and it is the basis of the socialist ideological system. Marxism has a rich connotation and profound ideas, and it mainly includes three major parts: materialistic dialectics, Marxian political economy, and scientific socialism [8]. It is an all-encompassing system of thought, and the richness of its principles is astonishing. It not only exposes that the world is material and has its specific laws of development but also puts forward the guiding method of discovering and understanding the laws of development of things in which practice is the only criterion for testing the truth. It also elaborates the development of human society, in which it reveals the essence of capitalism and the laws of development, depicts the emergence and development of socialism, and predicts that the final form of society is communism [9–15]. In the context of network globalization, are the basic principles of Marxism still applicable, have they been more widely disseminated, and have they correctly predicted the development of society? The study of these questions cannot be done without the support of data, and the development of computer technology out of data mining algorithms provides tools for research. In terms of the Internet realm, which reaches into every aspect of life, humanity has entered the era of big data. The scale of big data is so huge that it is impossible to directly obtain the desired information from it, and it needs to be processed and analyzed, collectively known as data mining [16]. Internet articles, social media text messages, and long and short videos are very important components of big data, and for the development of Marxist influence, the massive amount of Internet data that have accumulated over decades has considerable mining value [17]. To mine these data requires more knowledge about big data, which can be classified according to the distribution form of the data: structured big data, semi-structured big data, and unstructured big data. In the field of philosophical thought dissemination, unstructured big data have the highest percentage, which can reach 83.9%, and this percentage will continue to increase with the continuous improvement of data collection and storage [18–20]. This study, based on the understanding and recognition of the characteristics of big data, also requires technology as a means to bring the value of big data into play with the various algorithms that are popular and common today, and most of these algorithms are already relatively well developed and mature. The use of computer algorithmic techniques to mine data on philosophical ideas in the web and the ideologies developed from them is the only way to demonstrate the true value of big data in terms of the field of Marxist studies [21].To sum up, there are many shortcomings in the current research on the basic principles of Marxism in the context of big data. The first is the lack of concretization of philosophical ideas, which still remains at the stage of perceptual description, without clear assessment indicators to enable it to be defined at the rational level. Second, the research intensity and practice of it is not deep enough, and more scholars are needed to cut into it from different perspectives. Finally, it is the backwardness of research tools and objects. In the era of big data, data with a lot of noise that were neglected in the past and could not be mined because of insufficient technical power can be better utilized. The exploration and development of the basic principles of Marxism in modern times using the new tool of computer algorithms and the web as a vehicle is the way and method that this study focuses on. According to the characteristics and requirements of the big data era, this paper carries out Marxist theory education and realizes the thinking reform of Marxist theory education. In the era of big data, cloud computing technology, computer technology, and network technology are developing rapidly. It provides technical support for collection, computer and other information collection, and storage devices. It enables people to upload a large amount of data to the Internet, providing a digital platform for sharing educational information and educational resources. Second, the digital platform has a certain virtuality so that people can be more active in revealing their ideas. And big data technology can also analyze people’s thoughts. In this way, the education of Marx’s main theory can be spread according to people’s ideas. In the data age, there are not only a large number of data audiences but also a wide range of applications of big data technology, which has changed our lives. Make them the disseminators and receivers of information, which can expand the audience of Marxist theory education and make more people accept Marxist theory.


## 2. Identification and Preparation of Research Methods

### 2.1. Web Text Data Mining

First proposed by two scientists in the United States in 1995, text mining probability is a comprehensive technique that encompasses information retrieval and statistics, big data mining, machine black-box learning, and computer algorithms across many disciplines. Internet text mining converts unstructured data into structured and easy-to-read data and then discovers keywords, mines hidden information, acquires new knowledge, and finds connections between words and phrases from the structured data. With the development of statistical learning methods, especially after the 1990s, the number of online texts on the Internet has increased and the rise of machine learning disciplines. We gradually formed a set of classic methods to solve the problem of large-scale text classification. The main routine at this stage is artificial feature engineering + shallow classification model. Text mining will first preprocess the text dataset, then represent the unstructured data using text, and finally mine the structured text to finally get the required information.For Chinese text data within web-based articles, news, microblogs, comments and videos, and data cleaning needs to be performed first. Chinese text data cleaning includes removing deactivated words, restoring pinyin abbreviations, and eliminating meaningless phrases. Because computer algorithms cannot directly recognize and process Chinese utterances, the raw social media comment text data need to be formalized first. Aiming at the problem of visual clutter in large-scale geographic data visualization, a variety of visualization methods are proposed, mainly including filtering, aggregation and sampling. In the filtering scheme, attribute values are always considered to simplify the visualization of geographical data. In this method, data items with attribute values outside the scope of interest will be filtered out in the visualization of results. Three models are usually used to represent the Chinese text processing domain, namely dynamic vector space model, discrete probability model, and fuzzy concept model. In this experiment, the dynamic vector space model, which is relatively more stable and mature, is chosen to represent the social media comment data. In the dynamic vector space model each text is a collection of phrases, which are called features, and then the Chinese text can be represented in the form of vectors. It can be expressed by the following equation:(1)D=d1d2⋮dm=w11⋯w1n⋮⋱⋮wm1⋯wmn,where d is a text and m is the number of texts in this dynamic vector space model and then w is the weight of the text that is the same as each corner marker. The specific weights can be calculated by the following formul:(2)wtd=tft,d×idft=tft,d×logmdft+0.01.


### 2.2. Web Text Data Collection

Currently, the main content expressions on the Internet are web version and mobile version, and the mobile version is divided into two kinds of Android APP and iOS APP. Because of the closed nature of iOS system, it is difficult to capture the text data of its intrinsic APP, while Android system is relatively open, but it also needs many permissions to get the required text data completely. Therefore, the text data on Marxism in this study are mainly collected from the web side, which can be divided into static web pages and dynamic web pages according to the form of data. Static web pages are also known as HTML (HyperText Markup Language) format web pages, which are characterized by the fact that the content is already determined during the process of web page writing and does not change with changes in the background and user interaction. It is only necessary to use Python programming technology to crawl static web pages, compress and save all the content on these pages into txt text format, then remove the web tags, and store them in the database for use.In contrast to static web pages, dynamic web pages are much more complicated to collect data than static web pages because their contents change with background operations. Once the web content is published to the website server, whether or not users visit, the content of each static web page is saved on the website server. In other words, a static web page is a file actually saved on the server, and each web page is an independent file. The content of static web pages is relatively stable, so it is easy to be retrieved by search engines; dynamic web design usually includes some animation effects and dynamic special effects in the production, so as to make the website more dynamic and cause stronger visual impact to visitors. Reasonable application of dynamic effects can give the website a better experience and make visitors more clearly understand the content of the website. Figure1 shows the flow chart of dynamic web text data mining.Figure 1
Converting Marxism-related content in dynamic web pages to text data flow chart.From the flowchart of dynamic web page processing in Figure1, it can be seen that the dynamic content of the web page is first crawled in time by using the web text-crawling tool GooSeeker and then saved in xml text format that can be used by the Python algorithm and then the text is initially preprocessed using the Python algorithm. Unlike static web pages, dynamic web pages have a lot of scrolling information, so it is necessary to remove this part of text that is not meaningful to the study. In this study, ICTCLAS, a good word-splitting tool for Chinese, was chosen for processing. ICTCLAS can split the whole sentence into phrases and can recognize the semantics, actively removing, for example, advertisements and other content, leaving only city-related comments. The segmentation mainly divides phrases into three broad categories: nouns, verbs, and adjectival adverbs, as prepositions and pronouns have no real meaning. The selection of features for the phrases can be further optimized for the weight values of the text feature vector and then equation (2) can be rewritten as(3)wt,d=tft,d×logN/Nt+0.01∑t∈dtft,d×logN/Nt+0.012,tft,d in Equation (3) is the probability that a noun, verb, or adjectival adverb t appears in the text, and d and N are the total number of Marxist-related texts in the text database. Together, these texts form a feature space, and a text data set on its space is first defined as(4)T=x1,y1,x2,y2,⋯,xN,yN,xi∈Rn,yi∈+1,−1,i=1,2,⋯,N,where xi refers to the first i feature vector and yi refers to the dynamic class marker, which takes the value of +1 for positive cases and −1 for negative cases. Then, with the set text data set and hyperplane formula, the sample point xi,yi is taken and its several intervals can be found by the following formula:(5)γi=yiww⋅xi+bw.The maximumγ can be found and then the newly constructed objective function can be obtained by using the Lagrange multiplier method on it as follows:(6)Lw,b,α=12w2−∑i=1Nαiyiw⋅xi+b−1.The value ofαi is a Lagrange multiplier, which is greater than or equal to 0. When the sample point is in the feasible region, x, αi is set to infinity, and the constraint is satisfied. When the sample point x is in the infeasible region, the value can be expressed by the following equation:(7)θw=12w2,+∞..After obtaining this new objective function, the original text-capture problem is equivalent to(8)minw,bθw=minw,bmaxαi≥0Lw,b,α=p∗.It would be quite complicated to find the maximum value and then the minimum value for the above equation, so using Lagrangian duality, the positions of the maximum and minimum values are swapped to facilitate solving for both, and the transformed equation is(9)maxαi≥0minw,bLw,b,α=d∗.To maked∗=p∗, it is necessary that this text-crawling problem is a convex optimization problem and satisfies the KKT condition, which satisfies the following set of inequality equations:(10)αi≥0,yiwi⋅xi+b−1≥0,αiyixi⋅xi+b−1=0..The separation yields a maximum hyperplane of(11)w∗⋅x+b∗=0.The final classification decision function for the data set of crawled text can be obtained as(12)fx=signw∗⋅x+b∗.It is well known that Marxism specifies the formula for calculating surplus value in capitalism, which can be expressed as(13)mv=mL=sn.In the previous equation,m represents the surplus value in a factory under capitalism, v represents variable capital, L is the value of labor, s refers to surplus labor, and n represents the necessary labor of workers.This derived formula actually indicates in what proportion the working day or its valuable product is distributed between the capitalist and the worker. Thus, if one considers these formulas as direct expressions of capital’s own multiplication, one arrives at a false law: surplus labor or surplus value can never reach 100%. This introduces the variable of whether labor is remunerated or not, and the above equation is equivalent to(14)mv=mL=nonh,nonrepresents unpaid labor, while h represents paid labor.The Marxist formula for calculating capitalist surplus value can be applied to some of its counterparts in the textual crawl, making some texts that elaborate on surplus value more intuitive and easier to analyze. The law of surplus value refers to the law of surplus value production and capital proliferation. It reflects the essence of capitalist production and is the basic economic law of capitalism. In capitalist society, capitalists possess the means of production, and although workers have personal freedom, they are deprived of the means of production. Only when workers sell their labor as commodities and are purchased by capitalists can labor and means of production be combined for capitalist production. The basic economic laws of capitalism. It shows that the purpose of capitalist production is to produce and occupy surplus value.


## 2.1. Web Text Data Mining

First proposed by two scientists in the United States in 1995, text mining probability is a comprehensive technique that encompasses information retrieval and statistics, big data mining, machine black-box learning, and computer algorithms across many disciplines. Internet text mining converts unstructured data into structured and easy-to-read data and then discovers keywords, mines hidden information, acquires new knowledge, and finds connections between words and phrases from the structured data. With the development of statistical learning methods, especially after the 1990s, the number of online texts on the Internet has increased and the rise of machine learning disciplines. We gradually formed a set of classic methods to solve the problem of large-scale text classification. The main routine at this stage is artificial feature engineering + shallow classification model. Text mining will first preprocess the text dataset, then represent the unstructured data using text, and finally mine the structured text to finally get the required information.For Chinese text data within web-based articles, news, microblogs, comments and videos, and data cleaning needs to be performed first. Chinese text data cleaning includes removing deactivated words, restoring pinyin abbreviations, and eliminating meaningless phrases. Because computer algorithms cannot directly recognize and process Chinese utterances, the raw social media comment text data need to be formalized first. Aiming at the problem of visual clutter in large-scale geographic data visualization, a variety of visualization methods are proposed, mainly including filtering, aggregation and sampling. In the filtering scheme, attribute values are always considered to simplify the visualization of geographical data. In this method, data items with attribute values outside the scope of interest will be filtered out in the visualization of results. Three models are usually used to represent the Chinese text processing domain, namely dynamic vector space model, discrete probability model, and fuzzy concept model. In this experiment, the dynamic vector space model, which is relatively more stable and mature, is chosen to represent the social media comment data. In the dynamic vector space model each text is a collection of phrases, which are called features, and then the Chinese text can be represented in the form of vectors. It can be expressed by the following equation:(1)D=d1d2⋮dm=w11⋯w1n⋮⋱⋮wm1⋯wmn,where d is a text and m is the number of texts in this dynamic vector space model and then w is the weight of the text that is the same as each corner marker. The specific weights can be calculated by the following formul:(2)wtd=tft,d×idft=tft,d×logmdft+0.01.


## 2.2. Web Text Data Collection

Currently, the main content expressions on the Internet are web version and mobile version, and the mobile version is divided into two kinds of Android APP and iOS APP. Because of the closed nature of iOS system, it is difficult to capture the text data of its intrinsic APP, while Android system is relatively open, but it also needs many permissions to get the required text data completely. Therefore, the text data on Marxism in this study are mainly collected from the web side, which can be divided into static web pages and dynamic web pages according to the form of data. Static web pages are also known as HTML (HyperText Markup Language) format web pages, which are characterized by the fact that the content is already determined during the process of web page writing and does not change with changes in the background and user interaction. It is only necessary to use Python programming technology to crawl static web pages, compress and save all the content on these pages into txt text format, then remove the web tags, and store them in the database for use.In contrast to static web pages, dynamic web pages are much more complicated to collect data than static web pages because their contents change with background operations. Once the web content is published to the website server, whether or not users visit, the content of each static web page is saved on the website server. In other words, a static web page is a file actually saved on the server, and each web page is an independent file. The content of static web pages is relatively stable, so it is easy to be retrieved by search engines; dynamic web design usually includes some animation effects and dynamic special effects in the production, so as to make the website more dynamic and cause stronger visual impact to visitors. Reasonable application of dynamic effects can give the website a better experience and make visitors more clearly understand the content of the website. Figure1 shows the flow chart of dynamic web text data mining.Figure 1
Converting Marxism-related content in dynamic web pages to text data flow chart.From the flowchart of dynamic web page processing in Figure1, it can be seen that the dynamic content of the web page is first crawled in time by using the web text-crawling tool GooSeeker and then saved in xml text format that can be used by the Python algorithm and then the text is initially preprocessed using the Python algorithm. Unlike static web pages, dynamic web pages have a lot of scrolling information, so it is necessary to remove this part of text that is not meaningful to the study. In this study, ICTCLAS, a good word-splitting tool for Chinese, was chosen for processing. ICTCLAS can split the whole sentence into phrases and can recognize the semantics, actively removing, for example, advertisements and other content, leaving only city-related comments. The segmentation mainly divides phrases into three broad categories: nouns, verbs, and adjectival adverbs, as prepositions and pronouns have no real meaning. The selection of features for the phrases can be further optimized for the weight values of the text feature vector and then equation (2) can be rewritten as(3)wt,d=tft,d×logN/Nt+0.01∑t∈dtft,d×logN/Nt+0.012,tft,d in Equation (3) is the probability that a noun, verb, or adjectival adverb t appears in the text, and d and N are the total number of Marxist-related texts in the text database. Together, these texts form a feature space, and a text data set on its space is first defined as(4)T=x1,y1,x2,y2,⋯,xN,yN,xi∈Rn,yi∈+1,−1,i=1,2,⋯,N,where xi refers to the first i feature vector and yi refers to the dynamic class marker, which takes the value of +1 for positive cases and −1 for negative cases. Then, with the set text data set and hyperplane formula, the sample point xi,yi is taken and its several intervals can be found by the following formula:(5)γi=yiww⋅xi+bw.The maximumγ can be found and then the newly constructed objective function can be obtained by using the Lagrange multiplier method on it as follows:(6)Lw,b,α=12w2−∑i=1Nαiyiw⋅xi+b−1.The value ofαi is a Lagrange multiplier, which is greater than or equal to 0. When the sample point is in the feasible region, x, αi is set to infinity, and the constraint is satisfied. When the sample point x is in the infeasible region, the value can be expressed by the following equation:(7)θw=12w2,+∞..After obtaining this new objective function, the original text-capture problem is equivalent to(8)minw,bθw=minw,bmaxαi≥0Lw,b,α=p∗.It would be quite complicated to find the maximum value and then the minimum value for the above equation, so using Lagrangian duality, the positions of the maximum and minimum values are swapped to facilitate solving for both, and the transformed equation is(9)maxαi≥0minw,bLw,b,α=d∗.To maked∗=p∗, it is necessary that this text-crawling problem is a convex optimization problem and satisfies the KKT condition, which satisfies the following set of inequality equations:(10)αi≥0,yiwi⋅xi+b−1≥0,αiyixi⋅xi+b−1=0..The separation yields a maximum hyperplane of(11)w∗⋅x+b∗=0.The final classification decision function for the data set of crawled text can be obtained as(12)fx=signw∗⋅x+b∗.It is well known that Marxism specifies the formula for calculating surplus value in capitalism, which can be expressed as(13)mv=mL=sn.In the previous equation,m represents the surplus value in a factory under capitalism, v represents variable capital, L is the value of labor, s refers to surplus labor, and n represents the necessary labor of workers.This derived formula actually indicates in what proportion the working day or its valuable product is distributed between the capitalist and the worker. Thus, if one considers these formulas as direct expressions of capital’s own multiplication, one arrives at a false law: surplus labor or surplus value can never reach 100%. This introduces the variable of whether labor is remunerated or not, and the above equation is equivalent to(14)mv=mL=nonh,nonrepresents unpaid labor, while h represents paid labor.The Marxist formula for calculating capitalist surplus value can be applied to some of its counterparts in the textual crawl, making some texts that elaborate on surplus value more intuitive and easier to analyze. The law of surplus value refers to the law of surplus value production and capital proliferation. It reflects the essence of capitalist production and is the basic economic law of capitalism. In capitalist society, capitalists possess the means of production, and although workers have personal freedom, they are deprived of the means of production. Only when workers sell their labor as commodities and are purchased by capitalists can labor and means of production be combined for capitalist production. The basic economic laws of capitalism. It shows that the purpose of capitalist production is to produce and occupy surplus value.


## 3. Web Lexicon Building and Text Data Analysis

This study analyzes the relationship between the texts presented by the Internet as a vehicle and the understanding of Marxism by the population, which is reflected by the type of influence of Marxism and the impact on the population. From the results of the analysis of Internet text data, it can be obtained that the three interaction terms, the percentage of mentions, the percentage of applications, and the percentage of elaborations of Marxism are statistically strongly correlated with its influence. In order to verify the feasibility of the analysis and use of the Internet lexicon, this experiment draws the relationship between the expected cumulative probability and the observed cumulative probability of the model, as shown in Figure2.Figure 2
Relationship between expected cumulative probabilities of web texts and observed cumulative probabilities.It is obvious from Figure2 that most of the expected points are randomly distributed near the observation standard line. The two and one deviated points distributed on both sides of the baseline line, respectively, are analyzed in this study and learned to be invalid data, which can be ignored.Then the analysis of the texts crawled in the network was continued to obtain a subset of texts strongly correlated with the description of Marxist principles, and the results are represented by the P-R curve in Figure3.Figure 3
Schematic diagram of P-R curve and equilibrium point obtained by hybrid algorithm analysis data.The P-R graph visually shows the search completion rate and accuracy rate of Marxist-related texts on the Internet. The three balance points in the graph are a measure of the prediction accuracy of each sample subset, and the larger the value taken indicates the greater the relevance of this subset to Marxist principles.The number of texts about Marxist discussions in the Internet in the past years can visually reflect its influence and has certain reference significance for the exploration of its principles. Therefore, this study has conducted an analysis of the proportion of Marxist discussion texts to the texts of serious philosophical discussions in the Internet using the established Internet thesaurus, and the results of recent years are shown in Figure4.Figure 4
Proportion of Marxist discussion texts to serious philosophical discussion texts on the Internet.From 2017 to 2021 the percentage of mentions of Marxism in serious philosophical discussions has steadily increased from 62% to 78%, indicating that Marxism still has great influence and its value today. In a contemporary era where the division of human labor is becoming more and more fragmented and the hours of work are becoming longer, the ideas proposed and articulated by Engels and Marx are still applicable to a society with a highly developed Internet.The understanding and elaboration of Marxist principles show obvious differences between the proletariat and the bourgeoisie, and the establishment of a social class role lexicon is necessary to improve the accuracy of the experimental findings. The establishment of a role lexicon can further improve the efficiency of the analysis by eliminating phrases with small role differences and thus effectively reducing the capacity of the database. At the same time, it can filter out the phrases with social role characteristics and improve the accuracy of the analysis of basic Marxist principles. In this paper, 12,000 proletarian texts and 12,000 bourgeois texts related to Marxism were selected from the Internet text data to form the experimental objects. For the textual texts first, we use ICTCLAS word-splitting tool for word splitting, and then calculate the interaction information value of the two classes separately, and the result is that the maximum difference of interaction information value between the real classes is 6.28, so this experiment selects 0%, 10%, 20%, 30%, 40%, and 50% of the maximum value of interaction information value difference in the texts as the threshold value. Because phrases exceeding 50% of the maximum value are categorized into differential tremendous phrases, which have no reference significance for the study of basic Marxist principles, they are not considered. Six lexicons were created according to different proportions, the lexicons were vectorized using the dynamic vector space model above, and the classification results for 50 cross-validations of this data object are shown in Figure5.Figure 5
Experimental results of optimal threshold selection for bourgeois and proletarian thesaurus in Internet Marxism-related texts.The results of the above Figure5 show that as the maximum percentage of the difference increases, both the interaction information value and the accuracy rate show decreasing results. The accuracy rate is higher at the threshold percentage of 20%, and the difference between the interaction information values of the two orders is smaller, so the optimal threshold percentage is selected as 20%, and this ratio applied to the ranking of the Marxist principle study can improve the accuracy and scientificity of the results of gender classification pairs.Based on the results of the above analysis, this paper divides the texts published by proletarian and bourgeois societies in online platforms into different groups of people’s interpretation and application of Marxist principles according to the characteristic words in the understanding content that contains their expressions. Marxist class theory believes that the class of people is determined by relationship rather than by rank. The central axis of the class lies in the social organization of production rather than in the market. The analysis of class relations is rooted in the examination of the process of exploitation rather than the division of labor and technology or authority relations. For the bourgeoisie and the proletariat, for which there is a clear distinction, this study separately counts the number of texts produced by each of their groups in each quarter, and the results are shown in Figure6.Figure 6
Changes in the number of male and female social media comments by quarter.The results of the above graph show that the proletariat went to talk and pay attention to Marxism more in the second, third, and fourth quarters and in total and more frequently than the bourgeoisie. This is the same as the expectation of this experiment, Marxism is the guiding light for the proletariat, it finds the way to victory for the proletariat, and it is the comfort as well as the action program for the toiling masses exploited by capital.The ICTCLAS word-sorting tool can also determine the emotional color of adjectives in the text, which can translate the online text into a more intuitive rating of the acceptance of Marxism. The comments of this experiment are divided into one to three stars. The one-star comments mainly consider that Marxism is outdated, and its theory and connotation have shown fatigue due to the development of the Internet and technology. The three-star comments represent the authenticity of Marxism, and the people concerned are very satisfied with its principles. Figure7 shows the relationship between the number of emotionally expressed texts and the corresponding scores.Figure 7
Graph of online text volume and rating scales for different attitudes toward Marxism.The results of the above graph show a significant difference in the number of texts between the three scoring levels of the dynamic ANOVA. For these three different attitudes toward Marxism, the number of comments agreeing and disagreeing differs significantly, thus it can be known that different groups of people show very different attitudes on Marxism. It can be seen that the amount of textual data considering Marxism in the Internet context remains stable, and none of the ratings 1, 2, and 3 deny its philosophical achievements and practical guidance. And those who think it is still practical generally rate it at 3. The crowd that considers Marxism obsolete is as expected, with the majority giving it a score of 1. Marx has exerted an important influence on the development of the Internet in three aspects: broadening the research field, improving the analysis method, and innovating the basic theory. There are some commonalities between Marx’s theory and Internet culture in terms of research objects and research methods. It is the prerequisite for Marx to have an impact on social development. Marx studied social development from a higher angle than all sociologists. This is the fundamental reason why Marx can still have a great impact on the whole society.


## 4. Conclusion

This study establishes a set of text dictionaries related to Marxism and combines it with the residual value calculation formula to get a more intuitive and easier to use formula. From the results of network text data analysis, we can see that the three interactive terms of Marxism, namely, the percentage of mention, the percentage of application, and the percentage of elaboration, are statistically closely related to their impact. The expected error of Internet population approximately obeys the positive land distribution, and Internet text data are feasible in impact calculation. The statistical results show that from 2017 to 2021, the proportion of Marxism mentioned in serious philosophical discussions has steadily increased from 62% to 78%, indicating that Marxism still has great influence and value today. The continuous analysis of text data shows that when the threshold percentage is 20%, the accuracy rate is higher, while the difference between the interactive information values of the proletariat and the bourgeoisie is small. Therefore, the optimal threshold percentage is selected as 20%. Applying this proportion to the ranking of Marxist principle research can improve the accuracy and scientificity of gender classification results. Dynamic variance analysis shows that there is a significant difference in the number of texts between the three scoring levels of Marxism. For these three different attitudes toward Marxism, there are significant differences in the number of comments in favor of and against Marxism, which let us know that different people have very different attitudes toward Marxism. However, Marxism is a huge and profound system. This study only captures several aspects of the Internet for related research. I hope to further reveal the basic principles of Marxism in future research.



---

*Source: 1826988-2022-09-05.xml*