• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.38-49
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• 2021

This study analyzed the trend of the term 'false and exaggerated advertisement' in 5,141 newspaper articles from 1990 to 2019 using text mining methodology. First of all, we identified the most frequent keywords of false and exaggerated advertisements through frequency analysis for all newspaper articles, and understood the context between the extracted keywords. Next, to examine how false and exaggerated advertisements have changed, the frequency analysis was performed by separating articles by 10 years, and the tendency of the keyword that became an issue was identified by comparing the number of academic papers on the subject of the highest keywords of each year. Finally, we identified trends in false and exaggerated advertisements based on the detailed keywords in the topic using the topic modeling. In our results, it was confirmed that the topic that became an issue at a specific time was extracted as the frequent keywords, and the keyword trends by period changed in connection with social and environmental factors. This study is meaningful in helping consumers spend wisely by cultivating background knowledge about unfair advertising. Furthermore, it is expected that the core keyword extraction will provide the true purpose of advertising and deliver its implications to companies and related employees who commit misconduct.

• Journal of Internet of Things and Convergence
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• v.8 no.2
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• pp.41-48
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• 2022

This study tried to empirically explore which issues related to the social service agency for public(as below SSA), that is, social perceptions were formed, by using mess media related to the SSA. This study is meaningful in that it identifies the overall social perception and trend of SSA through public opinion. In order to extract media trend data, the search used the big data analysis system, Textom, to collect data from the representative portals Naver News and Daum News. The collected texts were 1,299 in 2020 and 1,410 in 2021, for a total of 2,709. As a result of the analysis, first, the most derived words in relation to the frequency of text appearance were 'SSA', 'establishment', and 'operation'. Second, as a result of the N-gram analysis, the pairs of words directly related to the SSA 'SSA and public', 'SSA and opening', 'SSA and launch', and 'SSA and Department Director', 'SSA and Staff', 'SSA and Caregiver' etc. Third, in the results of TF-IDF analysis and word network analysis, similar to the word occurrence frequency and N-gram results, 'establishment', 'operation', 'public', 'launch', 'provided', 'opened', ' 'Holding' and 'Care' were derived. Based on the above analysis results, it was suggested to strengthen the emergency care support group, to commercialize it in detail, and to stabilize jobs.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.451-462
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• 2019

Document-term frequency matrix is a type of data used in text mining. This matrix is often based on various documents provided by the objects to be analyzed. When analyzing objects using this matrix, researchers generally select only terms that are common in documents belonging to one object as keywords. Keywords are used to analyze the object. However, this method misses the unique information of the individual document as well as causes a problem of removing potential keywords that occur frequently in a specific document. In this study, we define data that can overcome this problem as proximity data. We introduce twelve methods that generate proximity data and cluster the objects through two clustering methods of multidimensional scaling and k-means cluster analysis. Finally, we choose the best method to be optimized for clustering the object.

• Journal of Internet of Things and Convergence
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• v.8 no.3
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• pp.29-40
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• 2022

The aim of this study was to analyze research trends of social service quality from 2007 to 2020 based on text mining and topic modeling. Our focus was to provide foundational materials for social service improvement by discovering the latent meaning of relevant research papers. We collected 97 scholarly articles on social service, social welfare service, and quality from RISS, and implemented two segments of text mining analysis. Our results showed that the first section included 38 papers and the second 59, indicating 6.9 articles annually. Word frequency results demonstrated that the common keywords of both sections were 'service', 'quality', 'social service', 'satisfaction', 'users', 'quality control', 'reuse', 'policy', 'voucher', etc. TF-IDF suggested that 'social service', 'satisfaction', 'users', 'customer satisfaction', 'revisiting', 'voucher', 'quality', 'assisted living facility', 'quality control', 'community service investment business', etc., were represented in both categories. Lastly, topic modeling analysis revealed that the first segment displayed 'types of care services', 'service costs', 'reuse', 'users based', and 'job creation', whereas the second presented 'service quality', 'public value', 'management system of human resources', 'service provision system', and 'service satisfaction'. Future directions of social service quality were discussed based on the results.

• Journal of Nutrition and Health
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• v.40 no.6
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• pp.566-575
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• 2007

The purpose of this research was to understand how individuals reflect on the frequency and quantity of foods that they consume. Participants selected 5 males and 15 females aged 30 years or older were first interviewed on the frequency of their food consumption. Then based on this data, they were given a cognitive interview using the method of verbal proving. The individual cognitive interviews were recorded with consent while being conducted after complete approval by the Seoul National University Institution Review Board. The recorded material was evaluated using a thematic analysis after transcribing them into text. By analyzing stages of reflection, the major barriers to make the device difficult are revealed: 1) More difficulty in remembering events over the course of a full year due to diversification in the types of food that people consume 2) difficulty calculating the average for seasonal foods 3) difficulty estimating the amount of consumption from the photos presented 4) difficulty estimating amount of consumption from the quantity presented 5) difficulty processing foods that people think are healthy and foods are unhealthy simultaneously 6) difficulty having to consider foods where target food goes in as an ingredient; 7) difficulties arising from having to increase frequency when the amount consumed is higher than the quantity that is presented 8) difficulty having to combine the frequency and quantity of each food item when numerous foods are clustered into one category. These findings show that the less participants were involved in cooking, the more diverse their eating habits were, and the more they tried to adhere to rules of filling out the questionnaire, the more it was difficult for them to come up with an answer to the question being asked. It therefore seems necessary to construct a Food Frequency questionnaire that is attentive to these problems that arise from the recall stages.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.415-427
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• 2021

Background and objective: This study examined the changes in the cultural trend of use for green infrastructure in Seoul due to COVID-19 pandemic. Methods: The subjects of this study are 8 sites of green infrastructure selected by type: Forested green infrastructure, Watershed green infrastructure, Park green infrastructure, Walkway green infrastructure. The data used for analysis was blog posts for a total of four years from August 1, 2016 to July 31, 2020. The analysis method was conducted keyword frequency analysis, topic modeling, and related keyword analysis. Results: The results of this study are as follows. First, the number of posts on green infrastructure has increased since COVID-19, especially forested green infrastructure and watershed green infrastructure with abundant naturalness and high openness. Second, the cultural trend keywords before and after COVID-19 changed from large-scale to small-scale, community-based to individual-based activities, and nondaily to daily culture. Third, after COVID-19, topics and keywords related to coronavirus showed that the cultural trends were reflected on appreciation, activities, and dailiness based on natural resources. In sum, the interest in green infrastructure in Seoul has increased after COVID-19. Also, the change of green infrastructure represents the increased demand for experience that reflects the need and expectation for nature. Conclusion: The new trend of green Infrastructure in the pandemic era should be considered in the the individual relaxations & activities.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.3
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• pp.39-51
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• 2020

This study will examine the social issues and consumer awareness of sharing parking through the method text mining. In this experiment, the topic by keyword was extracted and analyzed using TFIDF (Term frequency inverse document frequency) and LDA (Latent dirichlet allocation) technique. As a result of categorization by topic, citizens' complaints such as local government agreements, parking space negotiations, parking culture improvement, citizen participation, etc., played an important role in implementing shared parking services. The contribution of this study highly differentiated from previous studies that conducted exploratory studies using corporate and regional cases, and can be said to have a high academic contribution. In addition, based on the results obtained by utilizing the LDA analysis in this study, there is a practical contribution that it can be applied or utilized in establishing a sharing economy policy for revitalizing the local economy.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.