• Korean Journal of Environment and Ecology
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• v.33 no.5
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• pp.525-538
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• 2019

The purpose of this study was to analyze the habitat status and species diversity of benthic diatoms and estimate the applicability of TDI (Trophic Diatom Index) to obtain the basic data for the identification and management of created wetlands in the Nakdong River. We observed a total of 38 families and 173 species of benthic diatom during the survey period, and spring and autumn showed a similar number of species of 156 and 154, respectively. The result of the SOM (Self-Organizing Map) analysis showed that the distribution of benthic diatom was sensitive to environmental factors such as nutrient concentration and rainfall in each wetland. The cluster 1 was characterized by the survey sites of autumn mostly and consisted of points of high TDI, although the nutrients such as total phosphorus and total nitrogen were low, and the species number and abundance of diatoms were low. Conversely, cluster 4 was characterized by the survey sites of spring mostly and consisted of points of low TDI, even though total nitrogen was high. Considering that most of the created wetlands had the reduced inflow and outflow, the increased flow rate in the summer lowers nutrient values in autumn, and the species number and abundance of benthic diatom decreases due to the increase of turbidity, which reduces the light penetrations to the substrates. On the contrary, the TDI value is low in spring because the low water level causes insufficient substrate surface to the benthic diatoms, and it is too early for the establishment and development of saprophilous species. Although various studies have used TDI as an indicator for evaluating the habitat environment and water quality, it is not a good evaluation indicator in this study since the nutrient concentration in the wetlands mostly high as they have a low flow rate and are close to the stagnant area. Nevertheless, additional periodic surveys that comprehensively reflect the fact that the summer rainfall and inflow/outflow regulating function might affect the species diversity and distribution of benthic diatoms are necessary.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.187-204
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• 2016

Document classification based on emotional polarity has become a welcomed emerging task owing to the great explosion of data on the Web. In the big data age, there are too many information sources to refer to when making decisions. For example, when considering travel to a city, a person may search reviews from a search engine such as Google or social networking services (SNSs) such as blogs, Twitter, and Facebook. The emotional polarity of positive and negative reviews helps a user decide on whether or not to make a trip. Sentiment analysis of customer reviews has become an important research topic as datamining technology is widely accepted for text mining of the Web. Sentiment analysis has been used to classify documents through machine learning techniques, such as the decision tree, neural networks, and support vector machines (SVMs). is used to determine the attitude, position, and sensibility of people who write articles about various topics that are published on the Web. Regardless of the polarity of customer reviews, emotional reviews are very helpful materials for analyzing the opinions of customers through their reviews. Sentiment analysis helps with understanding what customers really want instantly through the help of automated text mining techniques. Sensitivity analysis utilizes text mining techniques on text on the Web to extract subjective information in the text for text analysis. Sensitivity analysis is utilized to determine the attitudes or positions of the person who wrote the article and presented their opinion about a particular topic. In this study, we developed a model that selects a hot topic from user posts at China's online stock forum by using the k-means algorithm and self-organizing map (SOM). In addition, we developed a detecting model to predict a hot topic by using machine learning techniques such as logit, the decision tree, and SVM. We employed sensitivity analysis to develop our model for the selection and detection of hot topics from China's online stock forum. The sensitivity analysis calculates a sentimental value from a document based on contrast and classification according to the polarity sentimental dictionary (positive or negative). The online stock forum was an attractive site because of its information about stock investment. Users post numerous texts about stock movement by analyzing the market according to government policy announcements, market reports, reports from research institutes on the economy, and even rumors. We divided the online forum's topics into 21 categories to utilize sentiment analysis. One hundred forty-four topics were selected among 21 categories at online forums about stock. The posts were crawled to build a positive and negative text database. We ultimately obtained 21,141 posts on 88 topics by preprocessing the text from March 2013 to February 2015. The interest index was defined to select the hot topics, and the k-means algorithm and SOM presented equivalent results with this data. We developed a decision tree model to detect hot topics with three algorithms: CHAID, CART, and C4.5. The results of CHAID were subpar compared to the others. We also employed SVM to detect the hot topics from negative data. The SVM models were trained with the radial basis function (RBF) kernel function by a grid search to detect the hot topics. The detection of hot topics by using sentiment analysis provides the latest trends and hot topics in the stock forum for investors so that they no longer need to search the vast amounts of information on the Web. Our proposed model is also helpful to rapidly determine customers' signals or attitudes towards government policy and firms' products and services.

• Journal of Korea Port Economic Association
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• v.25 no.1
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• pp.15-28
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• 2009

The purpose of this paper is to show the empirical extraction way of benchmarking ports for overcoming the shortcoming which the traditional DEA method has by using 20 Korean ports in 2003 for 2 inputs (birthing capacity, cargo handling capacity) and 2 outputs(Export and Import Quantity, Number of Ship Calls). Because DEA method has produced the limited set of efficient units which are reference to inefficient units respective of their differences in efficiency scores, it is necessary to adopt the more feasible benchmarking information according to the path analysis(tier or stratification). The core empirical results of this paper are as follows. Benchmarking ports against inefficient ports according to the tier analysis are that Masan Port(Janghang$\rightarrow$Jeju$\rightarrow$Seogoipo$\rightarrow$Yeosu), Jinhae Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando), Pohang&DonghaePort(Janghang$\rightarrow$Samcheonpo$\rightarrow$Pyungtag$\rightarrow$Samcheog), and Sogcho Port(Janghang$\rightarrow$Mogpo$\rightarrow$Seogoipo$\rightarrow$Wando). The policy implication to the Korean seaports and planners is that Korean seaports should introduce the new methods like Tier analysis of this paper for evaluating the port performance and enhancing the efficiency in short term, mid term, and long term according to the tier 3 stage, the tier 2 stage, and the tier 1 stage with original DEA stage.