A Trend Analysis of Floral Products and Services Using Big Data of Social Networking Services

Abstract

This study was carried out to analyze trends in floral products and services through the big data analysis of various social networking services (SNSs) and then to provide objective marketing directions for the floricultural industry. To analyze the big data of SNSs, we used four analytical methods: Cotton Trend (Social Matrix), Naver Big Data Lab, Instagram Big Data Analysis, and YouTube Big Data Analysis. The results of the big data analysis showed that SNS users paid positive attention to flower one-day classes that can satisfy their needs for direct experiences. Consumers of floral products and services had their favorite designs in mind and purchased floral products very actively. The demand for flower items such as bouquets, wreaths, flower baskets, large bouquets, orchids, flower boxes, wedding bouquets, and potted plants was very high, and cut flowers such as roses, tulips, and freesia were most popular as of June 1, 2019. By gender of consumers, females (68%) purchased more flower products through SNSs than males (32%). Consumers preferred mobile devices (90%) for online access compared to personal computers (PCs; 10%) and frequently searched flower-related words from February to May for the past three years from 2016 to 2018. In the aspect of design, they preferred natural style to formal style. In conclusion, future marketing activities in the floricultural industry need to be focused on social networks based on the results of big data analysis of popular SNSs. Florists need to provide consumers with the floricultural products and services that meet the trends and to blend them with their own sensitivity. It is also needed to select SNS media suitable for each gender and age group and to apply effective marketing methods to each target.

Changes in the Cultural Trend of Use by Type of Green Infrastructure Before and After COVID-19 Using Blog Text Mining in Seoul

“…Choo (2020) identified the trends in consumption markets with focus on keywords related to 'solitary consumption', and Kim and Han (2019) analyzed the leisure trend using a semantic network. Previous studies conducted quantitative and focused analysis through keyword frequency analysis (Park and Bae, 2018;Park and Oh, 2019;Park and Yeon, 2020;Shin et al, 2020;Woo and Suh, 2021), and supplemented qualitative and auxiliary analysis through related keyword analysis. Recently, studies are conducted using quantitative and qualitative analysis through topic modeling Park et al, 2021).…”

“…We collected blog posts from the most commonly used portal site (Naver) in Korea. Data for a total of 4 years was collected based on a previous study that collected data for three years in order to conduct research on prevailing trends (Park & Oh, 2019), which consisted of data for 3 years before December 15, 2019 (August 1, 2016 -December 31, 2019) and then data for 7 months after that (January 1 -July 31, 2020).…”

“…To this end, we selected 20 top frequency words for each park using the blog texts cleansed into a sequence of nouns and combined them by the four types of parks. Park and Oh (2019) We derived top 20 keywords for each type of green infrastructure combining the top 20 keywords of the green infrastructures that belong to each category of green infrastructure. Obtaining the simple sum of the frequency of each keyword has the issue of overestimating the keywords of sites with many words or posts collected.…”

“…Text mining studies had been used to identify massive research trends (Park and Bae, 2018 ;Byeon and Seo, 2020;Park et al, 2021;Choi and Choi., 2021) but recently they are actively conducted to identify the trends through perception changes and issues based on user experience (Kim, 2015;Kim and Jeon, 2018;Chae et al, 2021;Woo and Suh, 2018;Kong and Wang. 2019;Park and Oh, 2019;Do et al, 2020;Park and Yeon, 2020;Shin et al, 2020;Woo and Suh, 2020;Woo and Suh, 2021). Therefore, this study examines the changes in the main keywords of each type of green infrastructure before and after COVID-19 through blog text mining, derives key topics related to 'coronavirus', and analyzes related keywords, thereby capturing the changes in the cultural trend of use and providing policy implications.…”

The Effect of Social Media Content Types on User Reactions: Focused on a Case Study of Kew Gardens

“…24, No. 2, 2021 launched in the United States in 2010 that is tailored for mobile devices, and is characterized by featuring images as the main content rather than text (Kim, 2016;Park and Oh, 2019). It is a representative third-generation SNS that allows users to express their feelings and interact with other users by posting a post with a simple hashtag when they shoot an image that they want to share with their smart device, or upload an existing image (Nam et al, 2015).…”