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A Study on the General Public’s Perceptions of Dental Fear Using Unstructured Big Data
J Dent Hyg Sci 2023;23:255-63
Published online December 31, 2023;  https://doi.org/10.17135/jdhs.2023.23.4.255
© 2023 Korean Society of Dental Hygiene Science.

Han-A Cho and Bo-Young Park

Department of Dental Hygiene, Shinhan University, Uijeongbu 11644, Korea
Correspondence to: Bo-Young Park, https://orcid.org/0000-0002-4058-2186
Department of Dental Hygiene, Shinhan University, 95 Hoam-ro, Uijeongbu 11644, Korea
Tel: +82-31-870-3456, Fax: +82-31-870-3459, E-mail: yqqqm@hanmail.net
Received September 8, 2023; Revised October 19, 2023; Accepted October 26, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: This study used text mining techniques to determine public perceptions of dental fear, extracted keywords related to dental fear, identified the connection between the keywords, and categorized and visualized perceptions related to dental fear.
Methods: Keywords in texts posted on Internet portal sites (NAVER and Google) between 1 January, 2000, and 31 December, 2022, were collected. The four stages of analysis were used to explore the keywords: frequency analysis, term frequency-inverse document frequency (TF-IDF), centrality analysis and co-occurrence analysis, and convergent correlations.
Results: In the top ten keywords based on frequency analysis, the most frequently used keyword was ‘treatment,’ followed by ‘fear,’ ‘dental implant,’ ‘conscious sedation,’ ‘pain,’ ‘dental fear,’ ‘comfort,’ ‘taking medication,’ ‘experience,’ and ‘tooth.’ In the TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear. The co-occurrence analysis was used to explore keywords that appear together and showed that ‘fear and treatment’ and ‘treatment and pain’ appeared the most frequently.
Conclusion: Texts collected via unstructured big data were analyzed to identify general perceptions related to dental fear, and this study is valuable as a source data for understanding public perceptions of dental fear by grouping associated keywords. The results of this study will be helpful to understand dental fear and used as factors affecting oral health in the future.
Keywords : Big data, Conscious sedation, Data mining, Dental anxiety, Perception
Introduction

1.Background

Dental phobia is used interchangeably with dental fear and is defined as avoidance of dental treatments owing to a feeling of loss of self-control during dental treatments re-gardless of whether treatments are simple or complex1). A previous study showed that the prevalence of dental fear is 44.4% and 16.1% in children and adults, respectively2,3). Another study demonstrated that approximately 5% to 10% had extreme dental feal4).

People with high dental fear have lack of cooperation during dental treatment and are much more likely to avoid dental visiting by postponing or canceling their appoint-ments5,6). Missing regular dental care due to dental fear is associated with poorer oral health7). Managing dental care is required for oral health in the interest of patients. Moreover, dental care providers should strive to under-stand patients’ perception of dental fear in terms of patient management.

Since dental fear is related to cognition, people with dental fear seek information about how to reduce dental fear before dental visiting and can have power of subjective control over dental fear8). Therefore, investigating infor-mation sought by people with dental fear and analyzing the association between the information are expected to help investigating people’s perception of dental fear. Pre-vious studies reported that dental fear is associated with negative cognitive factors, such as past experiences, dental anesthesia, use of dental scaler, dental X-ray use, noise, pain, and cost9,10). Although these previous studies can be used to determine negative cognitive factors in patients with dental fear, there is a limitation in generalizing their findings to the public.

Text mining in big data analytics allows keyword extrac-tion from various types of data, pattern recognition, and pattern analysis. Text mining, which categorizes and visuali-zes a relationship and connection between the extracted key-words, entails a quantitative approach to the analysis volumi-nous textual data11). However, little research, using unstruc-tured data analysis technology, has been conducted in dentistry. In Korea, studies on dental hygienists’ perception of work performance12,13) and an online survey study that reviews patients having dental visits were reported14).

2.Objectives

This study aims to extract keywords with dental fear, investigate the relationship and connection between the keywords, and categorize and visualize people’s perception of dental fear. Future research will be conducted to esta-blish the structures of perceptions related to dental fear based on the outcomes from numerous unstructured data that were quantitatively analyzed.

Materials and Methods

1.Ethics statement

Since data used in this study did not contain personally identifiable information, this study was granted exemption from review by the Institutional Review Board of Shinhan University (IRB No.: SHIRB-202309-HR-212-03).

2.Study design

This study analyzed people’s perception of dental fear in Korea using the text mining technique in big data analytics that involves objectively analyzing further perception of dental fear in Korea.

3.Sample size

In this study, TEXTOM 6.0 was used to collect data from NAVER (www.naver.com; blogs, news, web docu-ments, NAVER IN, and academic information), Google (www.google.com; web documents, news, and Facebook), and Daum (web documents, blogs, news, and cafes), as the spatial range, to identify public perception of dental fear. The temporal range was set from 1 January, 2000, to 31 Dece-mber, 2022, for 13 years. This range was set based on the increasing pattern that studies on dental fear survey had increased in Korea and other countries since 200015).

4.Data collection and preprocessing process

Fig. 1 illustrates the three stages of the data analysis process. At the first stage, Textom 6.0 was used to collect data. Textom, a program that can efficiently analyze unstru-ctured big data, utilizes text mining techniques to collect significant keywords from numerous unstructured data. It is a useful collecting and analyzing tool to perform cen-trality analysis through frequency and semantic network16). It identifies core keywords from various types of texts, selects essential keywords, and develops a matrix to inve-stigate the frequency of co-occurrence of keywords. Textom can delete data unrelated to the study subject from the collected data and performed cleaning process to combine words with spaces and without spaces if they have the same meaning17). The core keyword was set as ‘dental fear’ to collect text. Large amounts of relevant data were extracted from web pages (NAVER and Google) using a mechanical method, and crawling was used to extract a total of 14,087 keywords. To analyze the collected data, text preprocessing was performed to segment morpheme and remove stopwords18). To minimize the impacts of dupli-cates, keywords were removed by performing URL dedu-plication. Mecab, a morpheme module for efficient analysis of Korean language, was used to extract consistent outcomes.

Fig. 1. Analysis process. TF-IDF: term frequency-inverse document frequency,CONCOR: CONvergence of iteration CORrealtion.

5.Data analysis

UCINET 6 was used for frequency analysis and term frequency-inverse document frequency (TF-IDF) analysis. TF-IDF measures how important a keyword appears in a document, and the higher the frequency, the more impor-tant that word is19). Degree centrality and eigenvector cen-trality were used for centrality analysis. Degree centrality measures the number of connections between keywords, and an influence between the connected nodes increases when the value is larger20). Eigenvector centrality mea-sures the importance of a node connection to reflect it to weighted node connection, which expands the concept of degree centrality20). At the third stage, co-occurrence anal-ysis was conducted using the UCINET 6 Netdraw tool to demonstrate the connectivity between the top 30 keywords collected by Textom. In co-occurrence analysis, study subjects expressed by two words are considered related each other if the two words are used in the same docu-ment21). CONCOR (CONvergence of iteration CORrealtion) analysis considers the correlation between the keywords which are in a similar structural location among the total network structure and is a clustering analysis that classifies keywords with high connection into one cluster22).

Results

1.Frequency of the top keywords and TF-IDF analysis

Among the keywords collected for analysis, the top 30 core keywords used for analysis are listed in the Table 1 with their frequency and TF-analysis results. In the top 10 keywords, the most frequently used keyword was ‘treat-ment,’ followed by ‘fear,’ ‘dental implant,’ ‘conscious se-dation,’ ‘pain,’ ‘dental fear,’ ‘comfort,’ ‘taking medication,’ ‘experience,’ and ‘teeth.’ In the top 10 keywords based on TF-IDF analysis, ‘dental implant’ had the highest value, followed by ‘conscious sedation,’ ‘dental fear,’ ‘patient,’ ‘treatment,’ ‘pain,’ ‘taking medication,’ ‘teeth,’ ‘endodontic treatment,’ and ‘comfort.’ The keywords took third (dental implant), fourth (conscious sedation), and sixth (dental fear) place in the frequency analysis were in the top three of the TF-IDF analysis, demonstrating that there are differences in the frequency and importance between the keywords used in the texts. Keywords ranked by TF-IDF analysis differed from the keywords ranked by frequency analysis, and this shows that the public was more likely to perceive the word, dental fear, negatively.

Keyword Frequency and TF-IDF Analysis on Dental Fear

Keyword Frequency Rank TF-IDF Rank
Treatment 6,288 1 2,001.3 5
Fear 5,779 2 1,484.0 14
Dental implant 4,779 3 4,400.6 1
Conscious Sedation 2,749 4 2,477.8 2
Pain 2,016 5 1,984.4 6
Dental fear 1,504 6 2,252.8 3
Comfort 1,409 7 1,754.7 10
Taking medication 1,275 8 1,973.6 7
Experience 1,166 9 1,701.2 11
Teeth 1,045 10 1,843.7 8
Patient 1,004 11 2,006.9 4
Reservation 991 12 1,587.2 12
Expectation 965 13 1,559.4 13
Endodontic treatment 721 14 1,780.5 9
Dentist 712 15 1,457.6 15
Anxiety 644 16 1,338.2 19
Children 583 17 1,409.3 18
Dental caries 582 18 1,420.5 17
Recommendation 577 19 1,440.6 16
Anesthesia 486 20 1,230.7 20
Cost 405 21 1,092.7 22
Wisdom tooth 397 22 1,102.7 21
Surgery 392 23 1,060.1 23
Overcome 368 24 1,058.0 24
Adult 310 25 869.4 25
Relieve 241 26 742.5 27
Advantages 233 27 779.7 26
Noise 211 28 673.0 28
Worry 196 29 654.7 29
Burden 195 30 652.5 30

TF-IDF: term frequency-inverse document frequency.



2.Centrality analysis

Table 2 shows the results of centrality analysis that was performed to identify connections between the keywords for dental fear in terms of characteristics between the networks.

Comparison of Keywords Frequency and Centrality on Dental Fear

Keywords Centrality degree Eigenvector degree
Index Rank Index Rank
Treatment 822.288 1 0.482 1
Fear 659.729 3 0.413 3
Dental implant 769.339 2 0.465 2
Conscious Sedation 459.542 4 0.321 4
Pain 349.593 5 0.247 5
Dental fear 146.949 11 0.094 13
Comfort 273.373 6 0.2 6
Taking medication 239.271 8 0.182 7
Experience 242 7 0.181 8
Teeth 139.661 12 0.096 11
Patient 124.576 13 0.096 12
Reservation 214.678 9 0.161 9
Expectation 212.034 10 0.159 10
Endodontic treatment 86.237 17 0.058 18
Dentist 83.966 18 0.059 17
Anxiety 90.525 15 0.062 15
Children 54.136 22 0.035 22
Dental caries 87.898 16 0.06 16
Recommendation 96.254 14 0.071 14
Anesthesia 62.881 20 0.044 20
Cost 74.254 19 0.055 19
Wisdom tooth 49.39 23 0.034 23
Surgery 54.915 21 0.038 21
Overcome 39.644 25 0.025 25
Adult 40.305 24 0.025 26
Relieve 34.237 27 0.024 27
Advantages 36.881 26 0.028 24
Noise 31.644 28 0.022 28
Worry 23.847 30 0.017 30
Burden 27.814 29 0.02 29


For centrality, we used degree centrality and eigen-vector centrality. Degree centrality that can measure the influences of activities of keywords was conducted, showing that ‘treatment’ has the highest number of connection, fol-lowed by ‘dental implant,’ ‘conscious sedation,’ and ‘pain.’ Top five keywords in the eigenvector centrality that weig-hted the correlation between the keywords were same as those in the degree centrality.

3.Co-occurrence analysis

Keywords related to dental fear have relativity. The frequency of co-occurrence of the keywords was investi-gated, showing that the number of word types appear together was 6,000, and the frequency of co-occurrence was 76,069. The keywords ‘fear’ and ‘treatment’ appeared together the most frequently, with 1,322 times, followed by the keywords ‘conscious’ and ‘dental implant,’ with 1,268 times and the keywords ‘treatment’ and ‘pain,’ with 1,088 (Table 3).

Co-Occurrence Network Analysis on Dental Fear

Frequency Percentage (%)
Fear → Treatment 1,322 1.74
Conscious Sedation →Dental implant 1,268 1.67
Treatment → Pain 1,088 1.43
Comfort → Dental implant 1,025 1.35
Dental implant → Experience 968 1.27
Experience → Comfort 956 1.26
Taking medication → Conscious Sedation 956 1.26
Pain → Taking medication 956 1.26
Dental implant → Expectation 954 1.25
Expectation → Reservation 953 1.25


4.CONCOR analysis

CONCOR analysis was performed to group the key-words as listed in Table 4 and shown in Fig. 2. The first group was related to common perceptions related to dental fear. Images related to the dentist are demonstrated via key-words, such as ‘patient,’ ‘teeth,’ and ‘dentists.’ Also, this group shows a combination of negative words (dental fear, endodontic treatment, worry, and anesthesia) and positive words (comfort). Accordingly, we can assume that various images for dental fear are present in the first group. The second group was associated with the following consi-derations that people need to individually overcome dental fear: ‘experience,’ ‘expectation,’ ‘recommendation,’ ‘taking medication,’ ‘comfort,’ ‘reservation,’ ‘pain,’ ‘surgery,’ ‘cost,’ and ‘dental fear.’ The third group includes perceptions related to wisdom tooth extraction as a detailed factor for dental factor. The high correlations between ‘wisdom tooth,’ ‘fear,’ ‘burden,’ ‘conscious sedation,’ and advantages.’ The last group consists of perceptions related to dental caries, as a detailed factor for dental fear. Keywords, such as ‘dental caries,’ ‘adult,’ ‘children,’ ‘anxiety,’ ‘noise,’ and ‘overcome,’ were highly correlated. According to the key-words of third and fourth groups, we can speculate that the public specifies and perceives dental fear especially through wisdom teeth extraction and cavity treatment.

Dental Fear CONCOR Analysis

Group Characteristic Keyword No
Group 1 Common perceptions related to dental fear Dental fear, Patient, Tooth, Dentist, Endodontic treatment, Worry, Anesthesia, Relieve 8
Group 2 Factors to consider to overcome dental fear Dental implant, Experience, Expectation, Recommendation, Taking medication, Comfort, Reservation, Pain, Surgery, Cost 10
Group 3 Perceptions related to wisdom tooth extraction Wisdom tooth, Fear, Burden, Conscious Sedation, Advantages, Treatment 6
Group 4 Perceptions related to dental caries Dental caries, Adult, Overcome, Anxiety, Children, Noise 6

No: number of words per group, CONCOR: CONvergence of iteration CORrealtion.



Fig. 2. Visualization of CONCOR ana-lysis. CONCOR: CONvergence of iteration CORrealtion.
Discussion

1.Key results and comparison with previous studies

This study analyzed unstructured big data using text mining techniques to identify public perceptions of dental fear. In this study, the most frequently used keywords rela-ted to dental fear were treatment, followed by fear, dental implant, conscious sedation, and pain. In TF-IDF analysis, the top three keywords were dental implant, conscious sedation, and dental fear (Table 1). This part elucidates that dental implant and conscious sedation are the most influe-ntial keywords for public perceptions related to dental fear. In co-occurrence analysis, ‘conscious sedation’ and ‘dental implant,’ taking second place, were repeated 1,268 times. This is elucidated by the fact that in the field of dental care, conscious sedation is actively recommended for those who have dental fear or need to control fears of having dental implant. From the dental care users’ perspective, people who have dental fear or fears of having dental implant sur-gery are more likely to be frequently exposed to informa-tion about conscious sedation while searching for relevant information. This can be the factor influencing decision process for dental treatment.

Conscious sedation, such as inhalation sedation, oral se-dation, and IV sedation, helps the patients to be in a trance- like state and can change patients’ responses to dental treat-ments to positive responses by reducing pain or emotional damage during dental care23). Based on the results of CONCOR analysis in this study, conscious sedation was classified into the group 3 with dental fear, burden, advan-tages, and wisdom tooth. This shows that conscious sedation is recognized as a benefit that reduces burden to surgical treatments. Patients have dental fear when they are having oral-surgical procedures, such as removal of wisdom tooth. Using conscious sedation for this situation will help redu-cing the patients’ anxiety24,25). A study by Kim et al.26) reported that pain was more reduced in the implant group with conscious sedation than the group without conscious sedation. Pain can be normally controlled during dental treat-ment or implant via local anesthesia, but fear of treatment is a psychological health issue27). Therefore, using con-scious sedation to ease nervous irritability is efficient to reduce fear28). Accordingly, dental care providers should consider actively informing the public of accurate infor-mation about conscious sedation and recommending cons-cious sedation for providing comfort care for patients with dental fear, if needed.

In co-occurrence analysis, ‘fear’ and ‘treatment,’ taking first place, were repeated 1,322 times, and ‘treatment’ and ‘pain,’ taking third place, were repeated 1,088. Also, in the CONCOR analysis, dentist, dental fear, patient, and teeth were classified into group 1. Based on this result, we can interpret this result that public recognizes dental treatment as pain and pain as fear. Although the importance of visi-ting the dentist on a regular basis for oral health care is being emphasized, people recognize the dentist as a scary place and visit the dentist only when they have pain in most cases29). Patients with advanced dental diseases due to mis-sing regular dental check-up owing to dental fear need treatment requiring anesthesia in most cases and are more likely to have pain on the course of treatment. Thus, this course of dental care is easily recognized as pain. A study by Shin et al.30) reported that the level of dental fear and worry is same as the level of pain and fear when people get a shot of anesthesia. Dentists should make an effort to ease dental patients’ dental fear by actively considering the methods controlling pain, such as using local anesthesia on insertion site so that patients will not feel pain, injecting anesthetic fluids that are stored similar with the body temperature, injecting anesthetics slowly and constantly, and injecting anesthetics using painless anesthesia technique31,32).

In this study, dental caries and anxiety are in the same classification (group 4). In previous studies investigating children’s perceptions of oral health33), dental fear was reported as an influencing factors for incidence of pain and caries. Moreover, a study conducted in adults demonstrated that the number of decayed teeth, missing teeth, and deca-yed-missing-filled-teeth (DMFT) was much higher when the level of dental fear was higher34). Self-management of dental fear will help improve oral health.

Previous studies on dental fear35-37) reported the subjects’ experiences with dental care or correlations between soci-odemographic characteristics and dental fear induction by conducting survey questionnaire. However, the present study analyzed texts collected via unstructured data, used the results to identify general perceptions related to dental fear, and grouped keywords that have correlations. In this regard, the results of this study are valuable as evidence to understand public perceptions of dental fear.

2.Limitations and suggestions for further studies

In this study, since data were collected only from social portal sites, such as NAVER and Google, data should be collected from more various sources in future research. Moreover, since the time range was set from 2,000 when selecting data, and data during the overall period were analyzed, we were not able to change in perceptions accor-ding to time change. Therefore, future research should be conducted by dividing the study period to analyze key-words of texts for dental fear by periods and to review changes in perceptions.

Acknowledgments

None.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Ethical Approval

The study was approved by the Institutional Review Board of Shinhan University (IRB No.: SHIRB-202309- HR-212-03).

Author contributions

Conceptualization: Bo-Young Park and Han-A Cho. Data acquisition: Han-A Cho. Formal analysis: Han-A Cho. Supervision: Han-A Cho. Writing-original draft: Bo-Young Park and Han-A Cho. Writing-review & editing: Bo-Young Park and Han-A Cho.

Funding

None.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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