ANALYSIS OF THE EMOTIONAL LOGIC OF

MEDIATISATION OF ANCIENT LITERATURE

ORIENTED ON OVERSEAS DISSEMINATION

OF CHINESE CULTURE

Bin Wang*

• College of Chinese Language and Literature, Qufu Normal University, Qufu,

Shandong, 273165, China

•wangbin2262023@163.com

Xin Xu

• College of Foreign Languages, Qufu Normal University, Qufu, Shandong, 273165,

China

Reception: 25 February 2024 | Acceptance:10 April 2024 | Publication: 18 May 2024

Suggested citation:

Wang, B. and Xu X. (2024). Analysis of the Emotional Logic of Mediatisation

of Ancient Literature Oriented on Overseas Dissemination of Chinese

Culture. 3C Empresa. Investigación y pensamiento crítico, 13(1), 273-289.

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ABSTRACT

This study analyzes the emotional logic of the mediatization of ancient literature

oriented to the overseas dissemination of Chinese culture by constructing an emotion

analysis model and an emotion logic model. The sentiment analysis model includes

key parts such as the model structure, the basis of sentiment analysis, and the

sentiment intensity and activation tuning value. Then, through the sentiment logic

model, the process of updating the sentiment subject and the system structure are

studied to better understand the mediatization process of ancient literature overseas.

Through the stage tasks of data crawling and sentiment categorization, rich literary

works and related information were obtained from multiple channels. In the correlation

analysis stage, taking literary themes as an example, it was found that the level of

emotional engagement was medium-high and correlated with a reading frequency

score of 7.0, and that there was a strong correlation between the emotional

experience of ancient literary works and the elements of cultural transmission.

Overseas readers' reading frequency during work/study leisure and before going to

bed accounted for 70% and 60%, respectively, revealing that ancient literature has a

high level of attention in readers' daily life, and providing an in-depth and clear

perspective for understanding the dissemination effect of literature in overseas.

KEYWORDS

Sentiment analysis model; sentiment logic; ancient literature; mediatization; data

crawling; sentiment classification

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INDEX

ABSTRACT .....................................................................................................................2

KEYWORDS ...................................................................................................................2

1. INTRODUCTION .......................................................................................................4

2. CONSTRUCTING A SENTIMENT ANALYSIS MODEL ...........................................6

2.1. Model structure ..................................................................................................6

2.2. Foundations of Sentiment Analysis ....................................................................7

3. CONSTRUCTING AN EMOTIONAL LOGIC MODEL ..............................................8

3.1. Emotional intensity and activation thresholds ....................................................8

3.2. Emotional Subject Renewal Process and Architecture ......................................9

3.3. Stage Tasks ......................................................................................................10

3.3.1. Data Crawling and Sentiment Classification .............................................10

3.3.2. Word Classification and Sentiment Word Determination ...........................11

4. ANALYSIS OF THE MEDIATIZATION OF ANCIENT LITERATURE ABROAD .....11

4.1. Data Acquisition and Selection .........................................................................11

4.2. Results of Sentiment Analysis of Literary Works ..............................................12

4.3. Analysis of the association between emotions and cultural communication

elements ...............................................................................................................13

4.4. Emotional changes and cultural communication events ..................................14

5. DISCUSSION ..........................................................................................................15

6. CONCLUSION ........................................................................................................16

REFERENCES ..............................................................................................................16

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1. INTRODUCTION

In reality, in the process of cultural development, as the development of ancient

Chinese literature has gone through a relatively long period of time, its own medium of

communication has also gone through various stages such as spoken medium written

medium, printed medium and electronic medium [1-2]. The oral communication form

of ancient Chinese literature has different distinctive features in different historical

periods [3]. In the rapid development of the present information age, the oral

communication forms of ancient Chinese literature have also become richer [4].

Combined with the actual situation of the development of ancient Chinese literature,

in-depth analysis of the oral communication forms of ancient Chinese literature has a

positive significance for the promotion of the dissemination and inheritance and

development of ancient Chinese literature. Literary communication media and

communication methods, the impact on literature is often also multidimensional [5].

The change of media not only has a close relationship with literature, but also has a

certain connection with the development of science and technology and society in the

same period. The spirit and appearance of literature in each era also have different

characteristics and changes under the role of media [6].

Digital technology and multimedia expression play an important role in cultural

inheritance, Zhang, J et al. proposed robust multi-view fuzzy clustering algorithm for

image segmentation of Chinese literati paintings to achieve effective extraction of

ancient paintings. Through effective extraction, the electronic and digital

transformation and preservation of literati paintings are realized. This preservation

method is more capable of preserving the artistry of literati paintings than traditional

scanning, and is of great value for the re-expression and dissemination of cultural

heritage [7]. Gu, L integrates and optimizes ancient literary information resources

through big data technology in order to improve the systematicity and completeness of

literature. The research mainly focuses on literary works and related collation,

annotation, and textual research results, and is committed to making it easier for

readers to find and browse ancient literature by dividing the scope of each subtopic

according to genre [8]. Zeng, Y et al. deepened the theoretical understanding of virtual

reality tourism from an emotional perspective, and by establishing a moderating

mediator model, examined how virtual reality tourism can enhance, through digital

technology, the tourists' experiential value and enhance people's sense of pride, thus

influencing tourists' behavioral intention of cultural dissemination [9]. Cui, C et al.

emphasized how to raise the younger generation's awareness of intangible cultural

heritage preservation and dissemination, and by taking the WeChat-based platform of

Hangzhou's traditional gastronomy and culture light game design as an example, they

demonstrated the skill of skillfully integrating intangible cultural heritage elements into

the game design, which offers a practical solution for the help of digital games to It

provides a practical example for promoting the inheritance of intangible cultural

heritage with the help of digital games [10].

Turton, A et al. proposed that Emotional Logic Coaching categorizes ELDP

statements, into two categories: personal and relational competency changes. It was

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found that the development of personal comprehension helps to enhance participants'

self-care in new and challenging environments and reduces dependence on

professional or other aspects. By measuring the outcomes of a truly values-based

action plan [11]. Tursunovich, R. I The main goal is to consider the point of view

direction in literary translation, focusing on how to accurately convey words and ideas

related to the language and culture. In order to better perceive cross-cultural

differences, translators need to understand and study the mentality of people in the

original language. The quality of translation can be improved by applying lexical,

grammatical and stylistic skills in translation practice and following translation norms

on-site [12]. Ashton, D explores the relationship between entrepreneurial orientation

and emotional labour, analysed through an empirical study of cultural organizations in

the UK. The paper examines emotional labor in the entrepreneurial process in terms

of two themes or relationships, namely emotional labor in patronage relationships and

emotional labor in audience relationships. The analysis of these themes highlights the

impact and consequences of ongoing emotional labor on cultural organizations and

cultural workers [13]. Widmann, T used a novel sentiment dictionary to analyze large

amounts of textual data, including over 700,000 press releases and tweets from three

European countries. The study found that populist parties are more likely to use

negative emotional appeals, such as anger, fear, disgust, and sadness, than

mainstream parties. And positive emotional appeals, such as joy, enthusiasm, pride,

and hope were relatively rare. It was also found that political actors adjusted the use

of emotional appeals according to the communication media and the status level of

the politicians in order to achieve different political objectives [14].

In order to explore the deeper level of changes in sentiment logic, a comprehensive

sentiment analysis framework is constructed by integrating LSTM, recurrent neural

network and Bayesian classification to explore the sentiment logic of ancient Chinese

literature in overseas dissemination. Firstly, LSTM and recurrent neural network

structures are used to capture the long-distance and short-distance emotional

dependencies in ancient literature to improve the accurate modeling of emotional

information. Subsequently, the concepts of emotional intensity and activation value

are introduced, which not only can more accurately measure the expressive power of

emotions in ancient literature, but also help to more deeply understand the impact of

emotions in the communication process. Finally, the dynamic change of the emotional

subject in ancient literature is modeled, taking into account the updating process of

the emotional subject and the overall architecture, as well as the construction of an

organic architecture to reflect the development of emotion in the overall narrative. At

the same time, the stage tasks of data crawling and emotion classification, as well as

word classification and emotion word determination are clarified to provide support for

the training and validation of the emotion logic model.

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2. CONSTRUCTING A SENTIMENT ANALYSIS MODEL

2.1. MODEL STRUCTURE

The powerful sequence modeling capability of LSTM can be applied to different

research directions to form a series of variants, and in this paper, we use recurrent

neural networks to analyze the emotional color in ancient literature [15]. Figure 1

shows two grid cell structures, Figure 1(a) shows the basic structure of LSTM cell,

which is the most common LSTM variant. Figure 1(b) shows the SentiSTM cell, which

is based on the standard LSTM with emotion gates added to realize the storage of

emotion values, whereby the emotion values of the text sequences will not be

forgotten with the increment of the time step, and thus the accuracy of the emotion

classification can be improved [16].

Figure 1. Cell structure

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Each LSTM cell consists of an input gate , a forgetting gate , an output gate ,

and a memory cell , with representing the cell input at the current moment and

representing the cell output at the current moment Assuming that the dimension of the

memory cell is , the LSTM can be formulated as follows:

(1)

(2)

where is the activation function sigmoid, is the dot product operation, and

are the coefficient matrices, is the bias vector, and are the computation of

the input gate, the forgetting gate, and the output gate, respectively, is the

computation of the MEMORY cell at moment , and is the output of the LSTM cell at

moment .

The SentiISTM model with the addition of the sentiment gate has two different

updating methods, i.e., the sentiment update of the sentiment gate versus the

update of the memory cell :

(3)

(4)

2.2. FOUNDATIONS OF SENTIMENT ANALYSIS

Due to the special structure of recurrent neural networks, when using them for

sentiment classification, it is necessary to consider how to use the hidden state of

each moment for the final sentiment classification [17]. An intuitive approach is to use

the hidden state of the last moment of the RNN as a feature for sentiment

classification, the hidden state of the last moment, theoretically encodes the

semantics of the entire input sequence in it, and the subsequent sentiment

classification uses this semantic encoding as an input to the Softmax classifier, and

the probability of the sentiment polarity belonging to class is calculated as:

(5)

is the parameter of Softmax classifier.

it=σ(Wixt+Uiht−1+bi)

ft=σ

(

Wfxt+Ufht−1+bf

)

ct=ft⊙ct−1+it⊙

tanh(Wcxt+Ucht−1+bc)

ot=σ(Woxt+Uoht−1+bo)

ht=ot⊙tanh(ct)

⊙

W∗

U∗

b∗

it,ft

st=σ(Wsxt+Usht−1+bs)

ct=ft⊙ct−1+st⊙ct−1+it⊙tanh(Wcxt+Ucht−1+bc)

p(y=c∣ht,U,b)=exp(htUc+bc)/

∑

l=1

exp(htUt+bl

)

U,b

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For texts of long length, encoding all semantic information into a fixed-length vector

will to some extent have a loss of information, which affects the effectiveness of

classification [18]. In order to utilize the semantic information of each moment, a Mean

pooling layer is added between the RNN and Softmax layers to average the hidden

states of each moment, i.e.:

(6)

Then, is used as an input to the Softmax classifier, unlike the RNN-last model

which uses only the last moment hidden state RNN-mean uses each moment's

hidden state for the final sentiment categorization by incorporating a Mean pooling

layer, which is equivalent to a single vote for the final categorization at each moment

with the same weight [19].

3. CONSTRUCTING AN EMOTIONAL LOGIC MODEL

3.1. EMOTIONAL INTENSITY AND ACTIVATION THRESHOLDS

The role and influence of emotion in the mediatization of literature is analyzed by

relating emotional messages to the elements of cultural communication in literary

works [20]. Emotion intensity is influenced by many factors, including internal affective

factors, such as emotional self-attenuation and external affective factors, stimulation

by external events [21]. These affective factors correspond directly to the

corresponding emotion generators and are categorized into excitatory and inhibitory

factors according to their strengthening or weakening effect on emotion intensity.

Assuming that there is basic emotion, the emotion intensity can be defined as:

(7)

where denotes the intensity value of the emotion at the moment, is the

decay function specifying the way in which the emotion decays, is the sum of

the total effects of all the emotion generators at the moment, and is the factor of

the influence of the emotion on the emotion , both inhibitory and excitatory. If the

emotion inhibits the emotion , then the inhibitory factor, if the emotion

strengthens the emotion , then the excitatory factor, and if the emotion has

no direct effect or on the emotion , then .

The intensity of a certain emotion reaches a certain level will activate this emotion,,

when the personality differences in the magnitude of the intensity of the emotion

directly affect the level of the activation threshold, which is described as:

∑

k=1

hk/

e,t

=Ψ(I

e,t−1

)+δ

)+∑

k,k≠i

Iei,t

Ψ0

δt(el)

λki

λij < 0

λij > 0

i=j

λij = 0

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(8)

where denotes the activation queue value, is the activation value constant,

which represents the average activation level of the individual, and denotes the

influence factor of personality on the affective value [22].

The affective state of the individual at moment is given by the vector , ,

i.e:

(9)

The emotional state of a literary work may change over time, and as the emotional

state changes, the emotional state vector is updated.

3.2. EMOTIONAL SUBJECT RENEWAL PROCESS AND

ARCHITECTURE

Cognition plays a key role in the process of emotion generation, but the cognitive

process is exceptionally complex, and there is currently no recognized model in

cognitive science and psychology. Through the synergistic action of the subject's

beliefs, desires, norms and other cognitive elements, according to the calculation and

judgment of the intensity of emotion and the rules about emotion generation that

already exist in the subject's knowledge, so that the subject will update his or her own

emotion and express the emotion through the corresponding behavior.... The process

of updating the subject's emotion includes the following steps:

The subject perceives the external environmental events through the

perception module, and the existing beliefs update their beliefs through the

belief update module.

2. According to the new beliefs, the subject interacts with the existing desires,

norms, preferences, etc. under the guidance of the emotional rule base,

calculates the emotional intensity, makes a judgment on the intrusion value,

and implements emotional activation.

3. A new emotional state is generated through the emotion update module acting

on the intelligent subject's emotions [23].

4. Under the action of new beliefs and new emotional state, the subject generates

new wishes through the wish update module, and then generates new goals

and intentions until the behavior is generated under the action of intention and

commitment to act on the external environment.

Embedding the emotion update module into the model of the original BDI subject,

we propose an emotion subject architecture based on the calculation and judgment of

ω= (1 + ε)ω0,ε∈[−0.5, 0.5]

ω0

Et=(e0,t,e1,t,⋯ei,t,⋯ek,t),i= 0, ⋯,k

i,t=f(Iei,t,ω)=

{1, ⋯,I

e,t

≥ω

0, ⋯,Ie,t<ω

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emotion intensity, which contains the above emotion update process, and the emotion

master map is updated as shown in Figure 2. The emotion update module in the

figure represents the cognitive evaluation part, and a subject with normal emotion will

continuously repeat the above process in the environment to make emotion update

and response to the changes in the external environment. The role of the emotion

master map update module is to update the emotion master map, a system that

records the subject's current emotional state and cognition of changes in the external

environment. The cognitive evaluation part involves the subject's cognitive evaluation

of changes in the external environment, including the subject's perception and

assessment of events, behaviors, or beliefs in the environment. Under normal

circumstances, the subject has a certain emotional base and makes corresponding

emotional updates to changes in the external environment. The subject will

continuously repeat the above process in the environment, which is a dynamic

process, and the subject's perception and emotion of the environment may constantly

change with time and external events, and this system helps the subject to better

adapt and respond to the changing environment.

Figure 2. Emotionally intelligent subject update architecture

3.3. STAGE TASKS

3.3.1. DATA CRAWLING AND SENTIMENT CLASSIFICATION

Run a circular web crawl of ancient literature to crawl the data into the web. At this

time there is no classification, labeling all the data mixed together.

First of all, the text of the web page obtained from the download is preprocessed to

remove web page tags, such as , etc., and remove meaningless symbols. In

this paper, ICTCLAS software is used to carry out word division and lexical labeling,

removing deactivated words, including pronouns, auxiliaries and so on. After

⟨a⟩⟨∣a⟩

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completing the above processing, a piece of literary text is represented as a string of

word combinations.

3.3.2. WORD CLASSIFICATION AND SENTIMENT WORD

DETERMINATION

For textual sentiment analysis this paper uses the sentiment word judgment

method and the classifier for words uses the plain Bayesian classifier [24]. The

following describes the sentiment word judgment and word classifier used in this

paper respectively.

The plain Bayesian classifier is based on Bayes' theorem as follows:

(10)

In the equation, denotes emotions, positive, negative and objective, and

denotes a type of overseas dissemination of literature network. For different emotions,

is the same, and in the training corpus of texts, it is assumed that the number of

tweets for several emotions is equal, so the size of

depends only on

, and is obtained.

For the classified corpus, the sentiment words are then used to determine and

analyze the utterance sentiment. After processing the textual information the

emotional tendency label of the text is obtained, and each ancient literary work carries

a label corresponding to it. Then the works with the same kind of label are stored in

the same folder, so that the collection of works with the same kind of emotional

tendency is obtained by classification.

4. ANALYSIS OF THE MEDIATIZATION OF ANCIENT

LITERATURE ABROAD

4.1. DATA ACQUISITION AND SELECTION

Ancient literature is selected from the pre-Qin to Qing dynasties, such as

poetry, prose, and opera.

Data sources include ancient literature, digital libraries, ancient literature

databases, etc. Digital libraries provide online access to a large number of

ancient literary works, while ancient literature databases focus on specific

periods or literary genres.

3. Works from different literary genres, authors, and themes were collected to

ensure diversity in the study.

p(q∣W) =

P(W∣q)P(q)

P(W)

p(q∣W)

P(W∣q)

p(q∣W)∼P(W∣q)

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4. Using LSTM and recurrent neural network sentiment analysis models, the

works of ancient literature were automatically sentiment labeled to extract the

sentiment information in them. The sentiment information is further verified and

adjusted through Bayesian classification to improve the accuracy of sentiment

analysis. Combined with theme modeling and other techniques, the contextual

relationship of the sentiment information is deeply excavated to reveal the

deeper level of sentiment logic in the literary works.

4.2. RESULTS OF SENTIMENT ANALYSIS OF LITERARY WORKS

To gain a deeper understanding of overseas readers' reading habits and emotional

engagement with ancient literature, in order to further reveal the emotional logic of the

mediatization of ancient literature oriented to the overseas dissemination of Chinese

culture. Table 1 shows the reading frequency of ancient literature by overseas readers

in different time periods. Overseas readers' reading frequency is relatively high in their

leisure time at work/study and before going to bed, which are 70% and 60%

respectively. It shows that ancient literature plays an important role in readers' daily

life and is widely used for relaxation and immersion in literary atmosphere. And this

group of readers showed a high degree of emotional engagement while reading,

covering a wide range of emotional elements, such as joy, sadness, anger and so on.

Medium-frequency readers are 2-3 times a week and once a week, between 5% and

18%, and this group of readers shows some emotional engagement while reading,

covering a medium range of emotional elements. Low-frequency readers were 2-3

times per month, with the lowest percentage of 2%, and this group of readers showed

relatively low emotional engagement while reading, covering a more limited range of

emotional elements. A relationship was found to exist between emotional engagement

and cultural communication elements.

Table 1. Frequency of reading of ancient literature abroad

Time period Percentage Emotionally invested

Leisure time at work/study 70 % High

Before going to bed 60 % Middle

Holidays 29 % Lower

On the way to work 18 % Middle

Almost every day. 80 % High

4-5 times per week 5 % Middle

2-3 times per week 5 % Middle

Once a week 4 % Lower

2-3 times per month 2 % Lower

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4.3. ANALYSIS OF THE ASSOCIATION BETWEEN EMOTIONS

AND CULTURAL COMMUNICATION ELEMENTS

As Chinese culture continues to spread overseas, ancient literary works, as

elements of cultural communication, carry rich emotional connotations and have

attracted extensive research interest. This section aims to reveal the associations

between emotions and cultural communication elements in order to understand more

comprehensively how emotions affect the communication effects of ancient literary

works overseas. The association between emotion and cultural communication

elements is shown in Table 2, and the level of emotional engagement of different

elements is obtained after emotional analysis of each cultural communication element,

which is categorized into low, medium and high levels. The reading frequency scores

of different cultural communication elements among overseas readers are expressed

in the form of 1-10 points, with higher scores indicating higher reading frequency.

Literary themes showed high scores of 7.0 for both emotional engagement level

and reading frequency, indicating that readers are highly interested and emotionally

engaged in literary themes. Literary geography, literary social background, and literary

values, emotional investment water: low, reading frequency scores of 0.5, 0.4, and

0.2, because of the relative abstraction or relatively low audience demand. Literary

cultural heritage emotional investment level in the reading frequency rating of 6.0,

showing that the literary work heritage traditional cultural elements can trigger the

interest and emotional investment of readers. Among the elements with higher reading

frequency ratings, such as literary themes, characterization, and plot settings, they all

show a higher level of emotional engagement. This suggests that in the process of

mediatization of ancient literary works, more compelling plots and profound

characterization may be important factors that prompt readers to have a strong

emotional experience.

Table 2. Association between emotions and elements of cultural communication

Cultural Communication Elements Emotional engagement level

Reading

Frequency Score

Literary Themes Medium-high 7.0

Characterization High 6.0

Literary Style Medium 2.9

Literary Historical Context Medium 1.8

Plot Setting High 8.0

Literary Language Medium High 0.5

Literary regions Low 0.5

Literary social context Medium-high 0.4

Literary values Medium-high 0.2

Literary and Cultural Heritage Medium 6.0

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4.4. EMOTIONAL CHANGES AND CULTURAL

COMMUNICATION EVENTS

Emotional change plays a crucial role in the interaction between readers and

literary works, and the close relationship between emotion and literary communication

is revealed through the emotional logic analysis of cultural communication events.

Table 3 shows the association between affective changes and cultural communication

events, covering multiple aspects of cultural communication events, including literary

festivals, movie screenings of literary adaptations, and traditional literary exhibitions.

The affective changes triggered by each cultural communication event were assessed

and categorized as low, medium, and high. The ancient literary works involved in each

cultural communication event were clarified, and the readers' emotional feedback,

such as enthusiasm, warmth, excitement, etc., during different cultural communication

events were recorded.

Readers felt enthusiasm and warmth when reading "A Dream of Red Mansions",

which inspired a strong interest in the literary festivals. Traditional literature exhibitions

showed a moderate level of emotional changes, and readers felt intoxicated and

reflective in The Book of Poetry. The translation of the new version of the Analects of

Confucius triggered a medium-high level of emotional change, with readers feeling

emotion and identification, showing that translation work has a positive impact on the

re-expression of traditional literature. The recitation triggered a medium-high level of

emotional change, with the ancient literary work Zhuangzi. Sense and resonance

were experienced in that conveying literature through sound can stimulate a profound

emotional experience. The digital display triggered a medium-high degree of

emotional change, and the overseas communication of Zuozhuan felt moved and

amazed, indicating that digital technology provides a new form of communication for

literary works.

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Table 3. Association between emotional changes and cultural communication events

5. DISCUSSION

Future research could focus on the feedback and interaction of overseas

audiences, and gain insight into the actual impact of literature in cross-cultural

communication by investigating and analyzing the emotional resonance and

interpretation of ancient literary works by overseas readers. This will help construct a

more comprehensive model of literary communication that considers two-way

influence and cultural dialog. In addition, with the development of digital technology,

future research can also make use of emerging technological means such as virtual

reality and digital media to explore how to present ancient literary works more vividly

and improve the emotional engagement and experience of overseas audiences.

Focusing on the communication impact of literary works in the context of international

political and economic changes, we will explore how ancient literature has become a

representative of cultural soft power at a specific historical moment, and what kind of

emotional logic effect it has on the shaping of national image and cognition.

Cultural Communication

Events

Degree of

emotional change

induced

Ancient Literary Works

Involved

Emotional Feedback

from Readers

Literary Festivals Medium-high Dream of the Red

Chamber Enthusiasm, warmth

Movie Adaptation of Literary

Works Released High Journey to the West Excited, expectant

Traditional Literature Exhibition Medium The Book of Poetry Enchantment,

reflection

New Translation of Ancient

Literary Works Medium-high The Analects of Confucius Sentiment,

recognition

Web Promotion of Ancient

Literary Works High The Water Margin Praise, concern

Ancient Literature Recital Medium-high Zhuangzi Feeling, resonance

Literature Forum Medium The Records of the Grand

Historian Discussion, exchange

Digital Display of Ancient

Literary Works Medium-high Zuo Zhuan Touching, marveling

Literary Education Promotion

Activities Medium Lun Heng Learning, Inspiring

Social Media Sharing of

Ancient Literature High Chu Ci Sharing, Liking

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6. CONCLUSION

Through constructing sentiment analysis and sentiment logic model, the following

points are drawn from the in-depth understanding of overseas readers' reading habits

and emotional commitment to ancient literary works:

Overseas readers' reading frequency during work/study leisure and before

going to bed are 70% and 60% respectively, indicating that ancient literature

plays an important role in readers' daily life. It is not only a reflection of the role

of literature in relaxing the body and mind and immersing oneself in the literary

atmosphere, but also implies that literature is widely used among overseas

readers.

2. The emotional engagement level of the element of literary and cultural heritage

is high, with a reading frequency score of 6.0. It indicates that in the process of

mediatization of ancient literary works, emphasizing the heritage of traditional

culture in literary works is an important factor in attracting readers.

"A Dream of Red Mansions" and literary festivals, the translation of the new

version of "The Analects of Confucius" and emotional changes, as well as

recitals and "Chuang Tzu" highlight the emotional impact of ancient literary

works in cultural communication events, and provide substantial data support

for the mediatization of literary works overseas.

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