Purpose: This study aims to empirically analyze how consumers perceive their consumption of the semiotic value from cosmetics that they purchased by using text-mining analysis techniques-word cloud analysis, semantic network analysis, and sensitivity analysis-and examining the subjective emotional big data that consumers have left as discourse reviews concerning the brand reputation and selection attributes of cosmetics in social media. Methods: R version 3.6.2-RStudio Version 1.4.1103 was used to collect and analyze the review data on the semiotic value experience that cosmetics consumers left after their purchases. The research was conducted in three stages: the pre-processing of collected data and setting of stopwords, the execution of modeling, and the result analysis. Results: The word cloud analysis evinced that the word "skin" appeared the most, followed by "purchase" and "nature". The fact that such keywords featured prominently suggests that cosmetics consumers described their experience of their purchased cosmetics based on a semiotic value. The semantic network analysis revealed that "nature", "product", "real," "cover," "use," and "skin cosmetics" had high levels of degree centrality, betweenness centrality, and closeness centrality. Finally, through the sensitivity analysis, 4,742 words showing positive reviews, 2,039 words showing negative reviews, and 14,146 showing neutral reviews were found. Conclusion: In the word cloud analysis, the top 30 keywords represented the purchase goal and selection attribute of cosmetics consumers well and are considerably important for consumption factors related to cosmetics. "Real", "product", and "skin cosmetics", all of which were high in betweenness centrality, degree centrality, and closeness centrality in the semantic network analysis, play an important role in spreading and connecting "meaning" toward particular cosmetics brands on a daily basis. Finally, the sensitivity analysis found that positive emotional reviews appeared approximately 2.33 times more often than negative ones.

Purpose: This study is a cannibalization analysis of a family brand which is extended downward by a brand portfolio expansion strategy in the low-price cosmetics market. Brands such as THE FACE SHOP and BEYOND by LG Household & Health Care, MISSHA and A'pieu by ABLE C & C, and innisfree and ETUDE HOUSE by AMORE PACIFIC were set as the evaluation alternatives. The Bass diffusion model was applied to analyze the cannibalization, and cross analysis was conducted as an additional multiple analysis method. By performing positioning analysis, simultaneous research was performed on cannibalization analysis, market encroachment with competitive brands, and market competitiveness. Methods: Data collection was conducted from March 20 to April 21, 2018. We collected data from adult women who had used low-price cosmetics more than once, through a convenience sampling method. We received 287 answers from the total of 300 surveys and used these for the final analysis. Thirteen insincere or incomplete answers were excluded. The collected data were cross-analyzed by SPSS statistical package program to identify the purchasing conditions of low-price cosmetics and future brand conversion probability. A positioning map was prepared using biplot analysis. For demographical analysis, frequency analysis was also performed. Additionally, Bass diffusion model analysis was performed using sales time series data of each alternative evaluation brand. Results: Cannibalization was expected in all brands evaluated by Bass diffusion model analysis. The entry ratio was expected to increase, since the coefficient of imitation is higher in the propagation of new products than that of innovation. Conclusion: The results of the cannibalization analysis of brands in the low-cost cosmetics market have implications for various fields, such as measuring the performance of an expanded brand portfolio in an intensely competitive structure, launching new products, launching new brands in a new market, analyzing the market competition structure of competitive brands, and devising strategies to defend against cannibalization.

Purpose: This study was performed to provide low-cost cosmetic companies with useful information to enhance their market competitiveness and decision making. We used a positioning map to demonstrate how repositioning the map strategy could enhance market competitiveness via benchmarking of a specific low-cost cosmetics brand which was relatively underestimated in the market. Methods: The data for this study were collected from March 20 to April 21, 2017. Adult women with histories of low-cost cosmetics use were recruited via convenience sampling. We distributed 300 questionnaires; 22 questionnaires with inadequate responses were excluded and 278 questionnaires were used as the final data, which were analyzed by frequency and biplot analyses using the SPSS statistical program. In addition, the analytic hierarchy process (AHP) statistical program was used to perform a pairwise comparison analysis. Results: When the MISSHA cosmetic brand was benchmarked and repositioned, its market share increased to second highest. There was a 7.2% difference from the innisfree cosmetic brand, which occupied the first place in market competitiveness. In the current positioning, MISSHA's market share was 20.2%, but its market share after repositioning was 27.76%, which reveals that its market share increased by 7.56%. Conclusion: The repositioning strategy for strengthening market competitiveness can act as an efficient problem solving method for allocation of restricted resources by repositioning. We were able to reposition a specific low-cost cosmetic brand through benchmarking, using evaluation factors of the market's most competitive low-cost cosmetic brand.

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Purpose: This study aimed to analyze the similarities and differences between each area using wordcloud analysis and semantic network analysis, which are text mining techniques. Further, confirmatory factor analysis will be conducted by crawling for word-of-mouth information on attribute reviews as satisfied, normal, or dissatisfied after purchases that are subjectively given by millennial generations.Methods: The R program version 4.1.2 was used as a big data collection and analysis tool, and text mining analysis was performed through preprocessing and stopword processing on the collected data. Further, using LISREL 8.80 we conducted confirmatory factor analysis on these results.Results: Wordcloud analysis revealed that the terms “skin,” “products,” and “skin” ranked first in the evaluation area of “satisfied,” “normal,” and “dissatisfied,” respectively. Additionally, using confirmatory factor analysis, the correlation between the three latent variables of satisfaction, normal, and dissatisfaction was differentiated.Conclusion: The similarities and differences between the domains obtained through wordcloud and semantic network analyses and derived by classifying individual emotional responses of millennial consumers in social media into satisfied, normal, and dissatisfied domains are considered very meaningful. The keywords derived with high centrality in the semantic network for each domain is then refined and introduced as an observation variable for confirmatory factor analysis in accordance with the purpose of the study; this is helpful in research development for causal analysis in the future.