Frontiers in Computing and Intelligent Systems 
ISSN: 2832-6024 | Vol. 2, No. 1, 2022 

 

61 

Study on the Correlation between Drivers' Anger 
Language and Driving Anger 

Anmengdie Li, Jinglei Zhang *, Kaili Wang 

Shandong University of Technology, Zibo Shandong 255000, China 

* Corresponding author: Jinglei Zhang. 

 

Abstract: In order to study the correlation between drivers' language and anger emotion, a questionnaire survey was conducted 

to collect drivers' language habits and select the anger keywords that drivers use more frequently. Then, the anger keywords were 

scored quantitatively from three aspects of anger degree, occurrence frequency and impact on driving safety, and the critical 

ratio, correlation, reliability and validity of the questionnaire were tested. The results show that the weight of "death" and "stupid" 

is higher than 0.1, and the weight of "God" and "drive" is less than 0.1, and the weight of "drive" is lower than 0.1. It can be seen 

that different anger languages have different degrees of representation effect on drivers' anger. This method can be used as one 

of the bases for identifying drivers' anger, which provides a theoretical basis for multi-modal drivers' anger recognition, and has 

certain practical significance for drivers' driving safety. 

Keywords: Driving language; Driving behavior; Driving in anger, Emotion recognition; Fuzzy comprehensive evaluation. 

 

1. Introduction 

In recent years, the process of urbanization has been 

accelerating, the traffic pressure continues to increase, and the 

level of traffic environment and traffic safety is not optimistic. 

In the road traffic system, people are the main factor leading 

to traffic accidents. Affected by the driving environment, 

drivers will lead to angry driving behaviors, including 

aggressive driving behaviors such as random lane change, 

forced overtaking, and running red lights. These behaviors are 

often accompanied by the use of rude gestures, verbal insults, 

threats, rough behavior, and even malicious damage to others' 

property, which will not only affect normal traffic driving, but 

also seriously affect social security. Because the angry 

behavior of drivers is often accompanied by the output of 

angry language, this study aims to analyze the correlation 

between driver's language and anger during driving, which is 

of great significance to automatically identify drivers' anger 

through computers and ensure traffic safety.  

 At present, in the research on driver's angry language and 

anger at home and abroad, comprehensive recognition 

research focuses on speech features rather than semantic 

features. 

In terms of voice features, Li Shingling et al. analyzed the 

spectral characteristics of the driver's voice, built the 

recognition model by optimized PNN (Probabilistic Neural 

Networks) based on Firefly Algorithm (Firefly Algorithm, 

FA), which has certain advantages over traditional methods. 

Wang wenjing used the mel frequency cepstrum coefficient 

(MFCC) as features to realize the road rage diagnosis system 

based on speech; and in noisy environment, the accuracy of 

anger recognition rates is 96.27% and 97.87% obtained on the 

RAVDESS and CASIA data sets respectively Zhang huiyun’s 

research summarized emotional speech recognition from the 

aspects of speech emotion corpus, emotion feature extraction 

and application of speech emotion recognition, listed 

common emotion classification methods Nguye et al. used 

cascading 3-dimensional neural networks to extract the voice 

and face behavior features, and mixed the modes; they used 

the DBN algorithm to achieve feature level fusion, and the 

recognition accuracy reached 90.85%. Zhang et al. used DBN 

fusion network to achieve model level fusion of voice 

information and face information, as a classification 

algorithm, support vector machine has a recognition accuracy 

of 85.97%. 

In terms of language characteristics, Gao chengji proposed 

a language emotion recognition method, which describes the 

English language sentiment using the two-dimensional space 

model of potency-activity, and uses the staged approach based 

on Gaussian mixture model and support vector machine to 

measure the emotional valence and emotion Activation for 

classification and identification, with the highest accuracy of 

96.9%.Chen xin proposed a metaphorical emotion 

identification method by compositing emotional scenes 

(MET-ES), this method constructs emotional represent at-ions 

of sentences by utilizing the emotional scenes of words, and 

depicts the emotional reasons by fusing emotional 

representations of word pairs, which are conducive to the 

model in order to correctly identify the emotions of sentences. 

Hu Qianjing studied the effect of emotion on driving behavior, 

established specific driving indicators significantly related to 

emotion from the physiological and behavioral levels, and 

finally proposed feasible strategies for reasonable 

compensation. Tim et al. conducted feature modeling in terms 

of language and acoustics, acoustically, statistical data are 

generated according to acoustic audio descriptors; 

linguistically, word and phrase models based on probability 

and entropy are used to analyze their applications in anger 

classification. Gaonka et al. designed tag semantics to better 

encode semantic information related to emotion. In order to 

solve the multi label problem of text emotion, Li et al. 

established a factor graph based on emotion labels and upper 

and lower instance dependencies, and designed a factor graph 

reasoning model. In order to solve the problem of insufficient 

annotation data in emotion recognition tasks and improve the 

results of resource poor Chinese emotion recognition, Zhang 

et al. used resource rich English corpora to construct English 

text emotion recognition tasks to help Chinese text emotion 

recognition. 

To sum up, anger language research focuses on speech 



 

62 

recognition, using typical speech feature parameters such as 

Mel-Frequency Cepstral Coefficients (MFCC), Linear 

Prediction Coefficients (LPC), Linear Predictive Cepstral 

Coefficients (LPCC) to model and complete anger emotion 

recognition; model performance is improved on the basis of 

face recognition, to prove the correlation between voice and 

emotion recognition. As for language research, domestic 

research is not extensive, and most of them prefer theoretical 

analysis of language texts. In order to further analyze the 

correlation between drivers' angry language and anger, the 

questionnaire survey method is used to select the typical 

anger language in drivers' angry driving, establish the rating 

system of anger words, explore the correlation between 

language words and anger, and provide theoretical support for 

the recognition of multimodal drivers' anger. 

2. Questionnaire investigation 

The text designed a questionnaire for two purposes. First, 

it is necessary to understand the driver's language habits and 

establish an angry language database so that angry language 

can be selected and graded through the result statistics; 

second, it is necessary to investigate and count which driving 

behaviors are more likely to irritate drivers during driving, so 

as to more effectively induce drivers to generate anger during 

the simulation driving experiment. Therefore, the 

questionnaire includes anger language collection 

questionnaire and anger language rating questionnaire. 

2.1. Angry language collection questionnaire 

In order to establish a anger language database that can 

accommodate a certain sample size, this paper designs a 

subjective driver anger language collection table, which 

includes the basic information of the driver, such as driver's 

gender, age, driving age, whether the driver can exp- ress 

anger in words during driving, and what angry language he 

can say. 

The questionnaire is distributed by electronic questionnaire 

using multi-stage stratified overall sampling method to ensure 

the reliability of the data. Table 1 shows the statistical results 

of the basic information of drivers. It can be seen that the pro-

portion of men and women in the sample is balanced, and the 

age group is mainly young, which is in line with the current 

trend of young drivers. The population with different driving 

ages is relatively balanced, reflecting the scientific and 

representative nature of this survey. 

Table 1. Statistics of driver's basic information 

Features Classification Proportion 

Gender 
Male 49.95% 

Female 50.05% 

Age 

18-25 years old 28.35% 

26-35 years old 21.62% 

36-45 years old 20.81% 

45-55 years old 19.51% 

56 years old and above 9.71% 

Driving age 

2 years and below 20% 

3-5 years 35.86% 

6-10 years 25.52% 

more than 10 years 18.62% 

A total of 743 angry words were collected in the 

questionnaire, through word frequency analysis of the text, 

420 angry words were obtained, such as "angry death" and 

"sick". Among them, 10 words appeared more than 20 times, 

and the rest appeared less than 8 times. There is a large gap in 

the data frequency, so the top 10 words of the frequency are 

selected as angry language keywords. Since the frequency of 

anger language keywords used among drivers can reflect the 

language habit characteristics of drivers when they are angry 

to some extent, the higher the frequency of use, the better the 

driver's anger can be represented. Therefore, this paper 

continues to use these 10 keywords for the rating of anger 

language. The complete spelling and frequency of the main 

keywords are shown in Fig. 1. 

87

44

40

35

34

34

30

28

22

21

7

6

SI

SHABI

WOCAO

MADE

KAICHE

FULE

KAO

YOUBING

FAN

FEIWU

SHENJINGBING

TAMADE

0 20 40 60 80

times

 keywords frequency

 
Fig. 1 Figure of Angry Word Frequency Statistics 

2.2. Angry language rating questionnaire 

This survey imitates the Likert 5 scoring method so that it 

can quantitatively evaluate keywords Evaluate the 10 anger 

keywords from three aspects: the degree of anger("extremely 

light", "slightly light", "average", "slightly heavy", 

"extremely heavy"), the frequency of occurrence("never", 

"occasionally", "generally", "often", "always"), and the 

impact on driving safety("extremely light", "slightly light", 

"average", "slightly heavy", "extremely heavy"), and assign 

the options one to five points in order to quantify the 

evaluation. A total of 642 questionnaires were distributed in 

the second stage, with an effective questionnaire rate of 

96.1%. Table 2 shows some examples of questionnaires. 

Table 2. Some examples of questionnaires 

1. If you say "death" in anger during driving, your anger level is 

A. extremely light (Score 1 point)  

B. slightly light (Score 2 point)  

C. average (Score 3 point)  

D. slightly heavy (Score 4 point)  

E. extremely heavy (Score 5 point) 
2. During driving, you think the frequency of angry words "death" is 

A. never (Score 1 point)  

B. occasionally (Score 2 point)  

C. generally (Score 3 point)  

D. often (Score 4 point)  

E. always (Score 5 point) 
3. In the process of driving, do you think the degree of influence of 

saying "death" in anger on driving safety is 

A. extremely light (Score 1 point)  

B. slightly light (Score 2 point) 

C. average (Score 3 point)  

D. lightly heavy (Score 4 point)  

E. extremely heavy (Score 5 point) 

2.2.1. Critical ratio analysis 

The total scores of the survey scale are arranged in 

descending order, and the first 30% is selected as the high 

group, and the last 30% is selected as the low group. The 

difference of each item between the high and low scores of 

the scale is tested by the method of independent sample t-test 

to obtain the critical ratio (CR) of the scale. The calculation 



 

63 

formula is as follows 

𝐶𝑅 =
(∑𝜆𝑖)

2

(∑ 𝜆𝑖)
2+∑𝛿𝑖

             (1) 

In formula (1), c is the factor load of each item, 𝛿𝑖 is the 

residual variance of each item. The results showed that the 

difference between the two groups was significant, the CR 

value was less than 0.04, and the item discrimination ability 

was good. 

2.2.2. Critical ratio analysis 

Table 3. Distribution table of correlation coefficient between each 
item and category grouping of the survey scale 

Project 

Category grouping 

Anger 

level 

group 

Frequency 

group 

Impact 

degree 

group 

Anger level 

group 
1.000 0.139 0.045 

Question1 0.574 0.142 -0.123 

Question 4 0.732 0.182 0.012 

Question 7 0.665 0.114 0.041 

Question 10 0.589 0.132 0.062 

Question 13 0.884 0.122 0.013 

Question 16 0.843 0.113 0.011 

Question 19 0.744 0.143 0.021 

Question 22 0.789 0.110 0.025 

Question 25 0.765 0.120 0.029 

Question 28 0.777 0.161 0.027 

Frequency 

group 
0.139 1.000 0.214 

Question 2 0.155 0.772 0.332 

Question 5 0.145 0.735 0.374 

Question 8 0.120 0.659 0.297 

Question 11 0.114 0.689 0.325 

Question 14 0.132 0.894 0.359 

Question 17 0.123 0.742 0.319 

Question 20 0.143 0.721 0.298 

Question 23 0.144 0.855 0.308 

Question 26 0.162 0.746 0.364 

Question 29 0.102 0.765 0.337 

Impact 

degree 

group 

0.045 0.214 1.000 

Question 3 0.012 0.288 0.772 

Question 6 -0.089 0.292 0.715 

Question 9 0.033 0.304 0.743 

Question 12 0.045 0.335 0.789 

Question 15 0.043 0.342 0.776 

Question 18 0.025 0.299 0.751 

Question 21 -0.016 0.332 0.699 

Question 24 0.012 0.300 0.803 

Question 27 0.036 0.315 0.798 

Question 30 0.034 0.314 0.810 

The questionnaire items were classified into anger degree 

group, frequency group and influence degree group according 

to the content of the questions, and the correlation between 

the three category item groups and between each small item 

question and category group was explored. The correlation 

coefficient between each item and category item group of the 

survey scale is shown in Table 3. It can be seen from the table 

that the correlation coefficient between the items of the scale 

is relatively significant, especially between the frequency 

group and the impact degree group, which indicates that the 

internal consistency between the items is good, and can 

provide satisfaction and reliability levels with good quality. 

2.2.3. Reliability analysis 

The text adopts Cronback's α Coefficient to measure the 

reliability to analyze the reliability and effectiveness of the 

questionnaire the formula is [18] 

𝛼 =
𝐾

𝐾−1
(1 −

∑𝑆𝑖
2

𝑆2
)            (2) 

In formula (2), 𝐾 indicates the number of items in the 

question, 𝑆𝑖
2is the variance within the question of the score of 

question 𝑖, 𝑆2is the variance of the total score of all questions. 

SPSS21.0 software was used to test the reliability of anger 

language rating questionnaire. There were 30 questions in the 

questionnaire. After excluding invalid questionnaires, there 

were 617 rows of valid data. Overall Cronback's α the 

coefficient is 0.732, and the reliability of the two retests is 

0.722 and 0.707 respectively. It can be considered that the 

scale is highly reliable, Cronback's of other dimensions α the 

coefficient and retest reliability are above 0.70, which 

indicates that the reliability of the scale is good. The 

inspection results are shown in Table 4. 

Table 4. Reliability test results 

 Anger 

level 

Frequency 

of occurrence 

Impact 

degree 

Overall 

scale 

Cronback’s 

α 
0.782 0.797 0.819 0.732 

Retest 

Reliability 1 
0.701 0.705 0.703 0.722 

Retest 

Reliability 2 
0.723 0.710 0.709 0.707 

Number of 

items 
10 10 10 30 

2.2.4. Validity analysis 

In this paper, we need to analyze the validity of the survey 

scale through structural validity analysis. The results showed 

that Bartlett's spherical test 𝜒2 value was 5721.099 

(P=0.000<0.01), indicating that it was suitable for factor 

analysis; KMO value is 0.799, indicating that factor analysis 

is effective. The mode matrix, eigenvalue and variance 

interpretation rotated by the maximum variance method are 

extracted by principal component analysis, and three common 

factors with eigenvalue greater than 1 are selected, as shown 

in the simplified gravel diagram in Fig.2. The abscissa is 30 

items, and the ordinate is its corresponding eigenvalue. It can 

be seen from the figure that the eigenvalue of three factors is 

significantly higher than 1, and the cumulative variance 

contribution rate of three common factors is 70.85%. 

According to the contents of items included in each factor, 

three factors are determined as anger degree, frequency of 

occurrence and impact on driving safety. The factor loads of 

each project are shown in Table 5. 



 

64 

0 5 10 15 20 25 30
0

1

2

3

4

5

6
e
i
g
e
n
v
a
l
u
e

number of components

 eigenvalue

 

Fig. 2 Simplified rubble diagram 

Table 5. Factor Load Table of Each Project 

Anger level 
Frequency of 

occurrence 
Impact degree 

project load project load project load 

Question 

1 
0.611 

Question 

2 
0.614 

Question 

3 
0.872 

Question 

4 
0.719 

Question 

5 
0.672 

Question 

6 
0.817 

Question 

7 
0.650 

Question 

8 
0.633 

Question 

9 
0.684 

Question 

10 
0.798 

Question 

11 
0.816 

Question 

12 
0.680 

Question 

13 
0.863 

Question 

14 
0.685 

Question 

15 
0870 

Question 

16 
0.632 

Question 

17 
0.635 

Question 

18 
0.681 

Question 

19 
0.533 

Question 

20 
0.899 

Question 

21 
0.802 

Question 

22 
0.655 

Question 

23 
0.702 

Question 

24 
0.877 

Question 

25 
0.810 

Question 

26 
0.709 

Question 

27 
0.676 

Question 

28 
0.663 

Question 

29 
0.898 

Question 

30 
0.725 

3. Evaluation analysis 

3.1. Fuzzy comprehensive evaluation 

Since the judgment of anger is subjective, fuzzy and 

difficult to quantify, the fuzzy comprehensive evaluation 

method is selected for analysis, it is characterized by clear and 

systematic results. Qualitative evaluation can be converted 

into quantitative evaluation according to the membership 

theory of fuzzy mathematics, that is, fuzzy mathematics can 

be used to make an overall evaluation of things or objects 

restricted by multiple factors. The research ideas of fuzzy 

comprehensive evaluation are as follows: 

(1) Determine the indicator set of the evaluation object, 

namely the factor set. Suppose there are n evaluation 

indicators, which can be expressed as: 

U={u1,u2,...,un}        (3) 

In formula (3), ui(i=1,2,…n) is the evaluation index. 

 (2) Determine the evaluation result set of the evaluation 

object, that is, the evaluation set. Suppose there are m 

evaluation grades, which can be expressed as: 

V={v1,v2,...,vm}                                   (4) 

 (3) Determine the weight set of evaluation indicators, that 

is, the weight set. Suppose there are n evaluation indicators, 

which can be expressed as: 

A= {a1, a2..., an}, ai>0, ∑ = 1𝑛
𝑖=1  (5) 

(4) Determine the membership function and calculate its 

membership degree, and form the factor evaluation matrix R, 

that is to say, suppose the evaluation of the ith index ui: 

Ri={ri1,ri2,...,rin} (6) 

R=

11 12 1

21 22 2

1 1

...

...

... ... ... ...

...

m

m

n n nm

r r r

r r r

r r r

 
 
 
 
 
   

(7) 

In formula (6), Ri(i=1, 2,…n)is a single factor fuzzy subset, 

In formula (7), R is the evaluation matrix on V. (5) multiplies 

the factor evaluation matrix by the weight set to obtain the 

comprehensive evaluation model: 

B=A∘R= (b1, b2,   , bn)          (8) 

3.2. Entropy weight method 

The entropy weight method is often used to determine the 

weight set if there is no authoritative data standard, it uses 

information entropy to calculate the entropy weight of each 

index according to the variation degree of each index, and 

gets more objective index weight after correction. The 

research idea of entropy weight method is as follows: 

(1) Determine indicator data matrix: 

If different indicator types are different, indicator forward 

transformation is required, that is, all indicators are unified 

into smaller and better indicators or larger and better 

indicators. Assuming that there are m projects to be evaluated, 

a total of n evaluation indicators are set, which can form an 

indicator data matrix: 

11 12 1

21 22 2

1 2

( )

m

m

ij m n

n n nm

x x x

x x x
X x

x x x



 
 
 = =
 
 
   

(9) 

(2) Data standardization 

In order to unify the dimensions of each evaluation index, 

the data needs to be standardized, in the standardization 

method, the maximum minimum standardization method is 

commonly used, and its calculation formula is: 

𝑋 ′ = 𝑧𝑖𝑗 =
𝑥𝑖𝑗 −𝑚𝑖𝑛( 𝑥𝑗)

𝑚𝑎𝑥( 𝑥𝑗) − 𝑚𝑖𝑛( 𝑥𝑗)

=
𝑥𝑖𝑗 −𝑚𝑖𝑛{ 𝑥1𝑗 , 𝑥2𝑗 , … , 𝑥𝑛𝑗}

𝑚𝑎𝑥{ 𝑥1𝑗 , 𝑥2𝑗 , … , 𝑥𝑛𝑗} − 𝑚𝑖𝑛{ 𝑥1𝑗 , 𝑥2𝑗 , … , 𝑥𝑛𝑗}
 

(10) 

(3) Calculating information entropy 

First, calculate the probability matrix P, that is, the 

proportion of the ith sample under index j, and the calculation 

formula is: 

𝑝𝑖𝑗 =
𝑧𝑖𝑗

∑ 𝑧𝑖𝑗
𝑛
𝑖=1

 (11) 

Then calculate the information entropy corresponding to 

each index, and normalize it to obtain its respective entropy 

weight, the calculation formula is: 

𝑒𝑗

= −
1

𝑙𝑛 𝑛
∑𝑝𝑖𝑗 𝑙𝑛( 𝑝𝑖𝑗),

𝑛

𝑖=1

𝑗

= 1,2, … ,𝑚 

(12) 

𝑑𝑗 = 1 − 𝑒𝑗  (13) 

𝑊𝑗 =
𝑑𝑗

∑ 𝑑𝑗
𝑚
𝑗=1

  (14) 

The formula (12) represents the size of information entropy, 

the greater the information entropy, the smaller the 



 

65 

corresponding amount of information; in Formula 13, 𝑑𝑗 is 

the value of information utility; in Formula 14, the entropy 

weight 𝑊𝑗  of each index is the result of normalization of 

information utility value. 

(4) Empowerment 

To give weight to the data, calculate the final score, the 

calculation formula is: 

𝑆𝑖 = ∑𝑊𝑗𝑧𝑖𝑗

𝑚

𝑗=1

 (15) 

4. Result analysis 

In order to conduct quantitative rating of anger for 10 anger 

keywords in the questionnaire, the results were analyzed 

according to the steps of fuzzy comprehensive evaluation. 

The evaluation indicators of the object include the degree 

of anger of the key words, the frequency of occurrence and 

the degree of impact on driving safety, so the factor set is 

taken as follows: 

U= {the degree of anger, the frequency of occurrence, 
the degree of impact on driving safety} 

The evaluation result is 10 angry keywords, so the 

evaluation set is taken: 

V={dead, stupid, Dammit, fuck, driving, taking, cor, sick, 

annoying, waste } 

Using entropy weight method to determine the weight of 

factor set, the value of m is 10, the value of n is 3, according 

to the entropy value calculation formula (14): 

1 2 3 4 5

6 7 8 9 10

0.1086, 0.0979, 0.1163, 0.1103, 0.0876,

0.1121, 0.1025, 0.1150, 0.0839, 0.0658

W W W W W

W W W W W

= = = = =

= = = = =
 

According to formula (15), 𝑆1 = 0.4766, 𝑆2 =
0.2317, 𝑆3 = 0.2917 

Therefore, the weights of the three indicators are 𝑆1 , 𝑆2 

and 𝑆3 respectively, and the weight set is determined: 

A={0.4766,0.2317,0.2917} 

The membership matrix of the three factors is: 

The degree of anger R1 equals: 

(0.1111,0.1245,0.0895, 0.0985, 0.0798, 0.0864, 0.1004, 

0.1145, 0.0921, 0.1032) 

The frequency of occurrence R2 equals: 

(0.1123, 0.1233, 0.0907, 0.0973, 0.0810, 0.0852, 0.1016, 

0.1133, 0.0933 ,0.1020) 

The degree of impact on driving safety R3 equals: 

(0.0866, 0.1000, 0.1140, 0.1230, 0.1122, 0.1188, 0.0680, 

0.0821, 0.0965, 0.0988)R1、R2、R3 Evaluation matrix of 

three fuzzy subsets: 

𝑅 = [
𝑅1
𝑅2
𝑅3

]

= [
0.1111 0.1245 0.0895 0.0985 0.0798 0.0864 0.1004 0.1145 0.0921 0.1032
0.1123 0.1233 0.0907 0.0973 0.0810 0.0852 0.1016 0.1133 0.0933 0.1020
0.0866 0.1000 0.1140 0.1230 0.1122 0.1188 0.0680 0.0821 0.0965 0.0988

] 

The comprehensive evaluation model is obtained by 

multiplying the factor evaluation matrix by the weight set: 

B=A∘R= (0.1042, 0.1171, 0.0969, 0.1054, 0.0895, 0.0956, 

0.0912, 0.1048,0.0937,0.1016) 

According to the model results, the weight distribution of 

10 keywords is shown in Figure 3, it can be seen intuitively 

in the figure that the weight of "dead", "stupid", "fuck", "sick" 

and "waste" is higher than 0.1, so these five keywords are 

listed as Level I angry words; the weight of "Dammit", 

"driving", "surrender", "cor" and "annoying" is less than 0.1, 

which is listed as Level II angry words. In the following 

research, anger language can be used as a feature vector for 

angry driving recognition system according to the 

classification. 

 

Fig. 3 Anger keyword rating weight analysis chart 

5. Conclusion 

This paper collects anger keywords commonly used by 

drivers through questionnaire survey, quantifies and scores 

anger keywords from three aspects of anger degree, frequency 

and impact on driving safety, determines the weight of three 

indicators using entropy weight method, and empowers anger 

keywords through fuzzy comprehensive evaluation. The 

conclusions are as follows: 

(1) The anger language rating questionnaire designed from 

three aspects of anger degree, frequency and impact on 

driving safety has high reliability and validity, which can be 

used as a reference for anger language collection. 

(2) Five first level anger words, such as "death" and 

"stupid", and five second level anger words, such as 

"Dammit" and "driving", were selected. It can be seen that 

different angry words and sentences have different degrees of 

representation effects on drivers' anger. This method can be 

used as one of the bases for the computer to automatically 

identify drivers' anger. 

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