• International journal of advanced smart convergence
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• v.11 no.2
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.179-184
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• 2024

Franchise are now a red ocean in Food industry and they need to find other options to appeal for their product, the uprising content, food tech. The franchises are working on R&D to help franchisees with the operations. Through this paper, we analyze the franchise interest on food tech and to help find the necessity of development for franchisees who are in needs with hand, not of human, but of technology. Using Textom, a big data analysis tool, "franchise" and "food tech" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 01 January, 2023 to 31 December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "food tech" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, a total of 10,049 words were derived, and among them, the top 50 keywords with the highest relevance and search frequency were selected and applied to this study. The top 50 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. By using big data analysis, it was found out that franchise do have interest on food tech. "technology", "franchise", "robots" showed many interests and keyword "R&D" showed that franchise are keen on developing food tech to seize competitiveness in Franchise Industry.

• Journal of Convergence for Information Technology
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• v.9 no.5
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• pp.1-6
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• 2019

As the mobile market expands, a variety of platforms are available to provide multimodal media content. Multimodal media content contains heterogeneous data, accordingly, user requires much time and effort to select preferred content. Therefore, in this paper we propose multimodal media content classification using keyword weighting for recommendation. The proposed method extracts keyword that best represent contents through keyword weighting in text data of multimodal media contents. Based on the extracted data, genre class with subclass are generated and classify appropriate multimodal media contents. In addition, the user's preference evaluation is performed for personalized recommendation, and multimodal content is recommended based on the result of the user's content preference analysis. The performance evaluation verifies that it is superiority of recommendation results through the accuracy and satisfaction. The recommendation accuracy is 74.62% and the satisfaction rate is 69.1%, because it is recommended considering the user's favorite the keyword as well as the genre.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.52-57
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• 2022

In this study, keywords from representative online portal sites such as Naver, Google, and Youtube were collected based on text mining analysis technique using Textom to check the changes in metqaverse after COVID-19. before Corona, it was confirmed that social media platforms such as Kakao Talk, Facebook, and Twitter were mentioned, and among the four metaverse, consumer awareness was still concentrated in the field of life logging. However, after Corona, keywords from Roblox, Fortnite, and Geppetto appeared, and keywords such as Universe, Space, Meta, and the world appeared, so Metaverse was recognized as a virtual world. As a result, it was confirmed that consumer perception changed from the life logging of Metaverse to the mirror world. Third, keywords such as cryptocurrency, cryptocurrency, coin, and exchange appeared before Corona, and the word frequency ranking for blockchain, which is an underlying technology, was high, but after Corona, the word frequency ranking fell significantly as mentioned above.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.41-46
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• 2022

The purpose of this study is to confirm and analyze the impact on consumers through big data keyword analysis on weak food. For data collection, web documents, blogs, news, cafes, intellectuals, academic information, and Google Web, news, and Facebook provided by Naver and Daum were used as analysis targets. The data analysis period was set from January 2018 to December 2021. For data collection and analysis, the frequency and matrix of keywords were extracted through Textom, a social matrix site, and the relationship and connection centrality between keywords were analyzed and visualized using the Netdraw function among UCINET6 programs. In addition, CONCOR analysis was conducted to derive clusters for similar keywords. As a result of analyzing yakseon food with keywords, a total of 35,985 cases of collected data were derived. Through this, it was confirmed that medicinal food affects consumers. Furthermore, if a business model is created and developed through yakseon food, it will be possible to lead the popularization of yakseon food.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.33-39
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• 2021

This paper aims to analyze lexical properties of keyword lists extracted from NLT Old Testament Corpus(NOTC), NLT New Testament Corpus(NNTC), and The NLT Bible Corpus(NBC) and identify that text dispersion keyness is more effective than corpus frequency keyness. For this purpose, NOTC including around 570,000 running words and NNTC about 200,000 were compiled after downloading the files from NLT website of Bible Hub. Scott's (2020) WordSmith 8.0 was utilized to extract keyword lists through comparing a target corpus and a reference corpus. The result demonstrated that text dispersion keyness showed lexical properties of keyword lists better than corpus frequency keyness and that the former was a superior measure for generating optimal keyword lists to fully meet content-generalizability and content distinctiveness.

• Journal of information and communication convergence engineering
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• v.19 no.1
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• pp.48-53
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• 2021

Herein, we propose a document analysis system that analyzes papers or reports transformed into XML(Extensible Markup Language) format. It reads the document specified by the user, extracts keywords from the document, and compares the frequency of keywords to extract the top-three keywords. It maintains the order of the paragraphs containing the keywords and removes duplicated paragraphs. The frequency of the top-three keywords in the extracted paragraphs is re-verified, and the paragraphs are partitioned into 10 sections. Subsequently, the importance of the relevant areas is calculated and compared. By notifying the user of areas with the highest frequency and areas with higher importance than the average frequency, the user can read only the main content without reading all the contents. In addition, the number of paragraphs extracted through the deep learning model and the number of paragraphs in a section of high importance are predicted.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.1-7
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• 2023

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.68-74
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• 2023

The food industry has been hit hard since the first outbreak of COVID-19 in 2019. However, as of April 2022, social distancing has been resolved and the restaurant industry has gradually recovered, interest in restaurant start-ups is increasing. Therefore, in this paper, 'restaurant start-up' was cited as a key keyword through social media big data analysis using TexTom, and word frequency and cone analysis were conducted for big data analysis. The keyword collection period was selected from May 1, 2022, when social distancing due to COVID-19 was lifted, to May 23, 2023, and based on this, a plan to develop chatbot services for restaurant start-ups was proposed. This paper was prepared in consideration of what to consider when starting a restaurant and a chatbot service that allows prospective restaurant founders to receive information more conveniently. Based on these analysis results, we expected to contribute to the process of developing chatbots for prospective restaurant founders in the future

• International Journal of Advanced Culture Technology
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• v.12 no.1
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• pp.270-275
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• 2024

Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.