• Information Systems Review
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• v.21 no.1
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• pp.91-111
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• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• Land and Housing Review
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• v.15 no.3
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• pp.113-127
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• 2024

This study explores shifts in real estate investment sentiment using media reports from 2012 to 2022, segmenting the market dynamics into three distinct cycles based on housing and land transaction indices. Leveraging 54 BigKinds media sources, we investigates 3,387 headlines and 8,544 body texts using LDA topic modeling. The results show that the first cycle (2012-2015 ) centered on apartment pre-sales, where policy changes influenced sentiment but did not consistently affect investment decisions. The second cycle (2016-2018) was characterized by interest rate hikes and rising property prices in Seoul, resulting in significant fluctuations in transaction volumes. The third cycle (2019-2022) encompassed the effects of COVID-19, market instability, and policy failures, leading to distorted and weakened investment sentiment. Each cycle demonstrated that policies, interest rates, and economic events significantly shaped investor sentiment, as reflected in media reports.

• The Journal of Bigdata
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• v.1 no.2
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• pp.35-44
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• 2016

Today there is a fierce competition between social commerce and multi-channel distribution in korea and it is need to do comparative analysis about success factors between social commerce and multi-channel distribution. Unlike the other studies that have only used survey method, this study analyzed the success factors between social commerce and multichannel distribution using text mining techniques. We expect that the result of the study not only gives the practical implication for making the competition strategy of the retailers but also contributes to the diverse extension research.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.131-137
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• 2018

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user's opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.85-97
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• 2021

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.689-697
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• 2024

According to the South Korean Ministry of Land, Infrastructure, and Transport, instances of defect dispute resolutions, primarily between construction contractors and apartment occupants, have been occurring at an annual average of over 4,000 cases since 2014 to the present day. To address the persistent issue of disputes between contractors and occupants regarding construction defects, it is crucial to use customer sentiment analysis to improve customer rights and guide construction companies in their efforts. This study presents a methodology for effectively managing customer complaints and enhancing feedback analysis in the context of defect repair services. The study begins with collecting and preprocessing customer feedback data. Semantic network analysis is used to understand the causes of discomfort in customer feedback, revealing insights into the emotional sentiments expressed by customers and identifying causal relationships between emotions and themes. This research combines text mining, and semantic network analysis to analyze customer feedback for decision-making. By doing so, defect repair service providers can improve service quality, address customer concerns promptly, and understand the factors behind emotional responses in customer feedback. Through data-driven decision-making, these providers can enhance customer rights and identify areas for construction companies to improve service quality.

• The Journal of Information Systems
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• v.26 no.1
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• pp.21-51
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• 2017

Recently there have been an increasing number of companies suffering a negative brand image in the major media. Thompson et al. (2006) defined this as "$Doppelg{\ddot{a}}nger$ Brand Image." The images mentioned above have been created and propagated on Internet communities, which are one of the major paths of online spreading. This study will empirically analyze the effect of each $Doppelg{\ddot{a}}nger$ brand image on the customer's brand attitude, using a text-mining method focusing on "A company"'s case. This study will also cover the change in customer brand attitudes related to the company's correspondence in a situation in which the $Doppelg{\ddot{a}}nger$ brand image exists. In addition, the study will determine the presence of a priming effect after the spread of the $Doppelg{\ddot{a}}nger$ brand image. To that end, we collected 974 comments from 94,889 posts and A's official blogs related to A from B community, the largest automobile community site in Korea. Through this investigation, we obtained the following results. First, there was a significant difference in the ratio of negative sentiment of internet community before and after $Doppelg{\ddot{a}}nger$ brand image. Second, with regard to the topic modeling, the ratio of articles including negative topics increased and the other article ratio decreased over time. Finally, we found that there is a priming effect about negative brand image of "A company."

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.41-50
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• 2020

Sentimental analysis begins with the search for words that determine the sentimentality inherent in data. Managers can understand market sentimentality by analyzing a number of relevant sentiment words which consumers usually tend to use. In this study, we propose exploring performance of feature selection methods embedded with Particle Swarm Optimization Multi Objectives Evolutionary Algorithms. The performance of the feature selection methods was benchmarked with machine learning classifiers such as Decision Tree, Naive Bayesian Network, Support Vector Machine, Random Forest, Bagging, Random Subspace, and Rotation Forest. Our empirical results of opinion mining revealed that the number of features was significantly reduced and the performance was not hurt. In specific, the Support Vector Machine showed the highest accuracy. Random subspace produced the best AUC results.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.199-207
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• 2020

Recently, big data analysis has drawn attention in various fields as it can generate value from large amounts of data and is also used to run political campaigns or predict results. However, existing research had limitations in compiling information about candidates at a high-level by analyzing only specific SNS data. Therefore, this study analyses news trends, topics extraction, sentiment analysis, keyword analysis, comment analysis for the 2017 presidential election of South Korea. The results show that various topics had been generated, and online opinions are extracted for trending keywords of respective candidates. This study also shows that portal news and comments can serve as useful tools for predicting the public's opinion on social issues. This study will This paper advances a building strategic course of action by providing a method of analyzing public opinion across various fields.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.