Comparing Machine Learning Classifiers for Movie WOM Opinion Mining

( Yoosin Kim ),( Do Young Kwon ),( Seung Ryul Jeong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.8

Nowadays, online word-of-mouth has become a powerful influencer to marketing and sales in business. Opinion mining and sentiment analysis is frequently adopted at market research and business analytics field for analyzing word-of-mouth content. However, there still remain several challengeable areas for 1) sentiment analysis aiming for Korean word-of-mouth content in film market, 2) availability of machine learning models only using linguistic features, 3) effect of the size of the feature set. This study took a sample of 10,000 movie reviews which had posted extremely negative/positive rating in a movie portal site, and conducted sentiment analysis with four machine learning algorithms: naive Bayesian, decision tree, neural network, and support vector machines. We found neural network and support vector machine produced better accuracy than naive Bayesian and decision tree on every size of the feature set. Besides, the performance of them was boosting with increasing of the feature set size.

Competitive intelligence in Korean Ramen Market using Text Mining and Sentiment Analysis

( Yoosin Kim ),( Seung Ryul Jeong ) 한국인터넷정보학회 2018 인터넷정보학회논문지 Vol.19 No.1

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers’ opinions and competitive advantage in the competing market. Analyzing consumers’ opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers’ opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.

Practical Text Mining for Trend Analysis: Ontology to visualization in Aerospace Technology

( Yoosin Kim ),( Yeonjin Ju ),( Seonggwan Hong ),( Seung Ryul Jeong ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.8

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

Text Mining and Sentiment Analysis for Predicting Box Office Success

( Yoosin Kim ),( Mingon Kang ),( Seung Ryul Jeong ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.8

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers’ sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

Comparing Consumer Emotion in Social Media : Rivals in the Korean Ramen Market

Yoosin Kim,Mingon Kang,Seung Ryul Jeong 한국경영정보학회 2016 한국경영정보학회 학술대회논문집 Vol.2016 No.6

Opinion-Mining Methodology for Social Media Analytics

( Yoosin Kim ),( Seung Ryul Jeong ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.1

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers` opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

Visualizing the Results of Opinion Mining from Social Media Contents

Yoosin Kim(김유신),Do Young Kwon(권도영),Seung Ryul Jeong(정승렬) 한국지능정보시스템학회 2014 지능정보연구 Vol.20 No.4

Web2.0의 등장과 함께 급속히 발전해온 온라인 포럼, 블로그, 트위터, 페이스북과 같은 소셜 미디어 서비스는 소비자와 소비자간의 의사소통을 넘어 이제 기업과 소비자 사이의 새로운 커뮤니케이션 매체로도 인식되고 있다. 때문에 기업뿐만 아니라 수많은 기관, 조직 등에서도 소셜미디어를 활용하여 소비자와 적극적인 의사소통을 전개하고 있으며, 나아가 소셜 미디어 콘텐츠에 담겨있는 소비자 고객들의 의견, 관심, 불만, 평판 등을 분석하고 이해하며 비즈니스에 적용하기 위해 이를 적극 분석하는 단계로 진화하고 있다. 이러한 연구의 한 분야로서 비정형 텍스트 콘텐츠와 같은 빅 데이터에서 저자의 감성이나 의견 등을 추출하는 오피니언 마이닝과 감성분석 기법이 소셜미디어 콘텐츠 분석에도 활발히 이용되고 있으며, 이미 여러 연구에서 이를 위한 방법론, 테크닉, 툴 등을 제시하고 있다. 그러나 아직 대량의 소셜미디어 데이터를 수집하여 언어처리를 거치고 의미를 해석하여 비즈니스 인사이트를 도출하는 전반의 과정을 제시한 연구가 많지 않으며, 그 결과를 의사결정자들이 쉽게 이해할 수 있는 시각화 기법으로 풀어내는 것 또한 드문 실정이다. 그러므로 본 연구에서는 소셜미디어 콘텐츠의 오피니언 마이닝을 위한 실무적인 분석방법을 제시하고 이를 통해 기업의사결정을 지원할 수 있는 시각화된 결과물을 제시하고자 하였다. 이를 위해 한국 인스턴트 식품 1위 기업의 대표 상품인 N-라면을 사례 연구의 대상으로 실제 블로그 데이터와 뉴스를 수집/분석하고 결과를 도출하였다. 또한 이런 과정에서 프리웨어 오픈 소스 R을 이용함으로써 비용부담 없이 어떤 조직에서도 적용할 수 있는 레퍼런스를 구현하였다. 그러므로 저자들은 본 연구의 분석방법과 결과물들이 식품산업뿐만 아니라 타 산업에서도 바로 적용 가능한 실용적 가이드와 참조자료가 될 것으로 기대한다. After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors" opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean “Ramen” business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the resu

Visualizing the Results of Opinion Mining from Social Media Contents

Yoosin Kim,Do Young Kwon,Seung Ryul Jeong 한국지능정보시스템학회 2014 한국지능정보시스템학회 학술대회논문집 Vol.2014 No.11

뉴스와 주가 : 빅데이터 감성분석을 통한 지능형 투자의사결정모형

김유신(Yoosin Kim),김남규(Namgyu Kim),정승렬(Seong Ryoul Jeong) 한국지능정보시스템학회 2012 지능정보연구 Vol.18 No.2

누구나 뉴스와 주가 사이에는 밀접한 관계를 있을 것이라 생각한다. 그래서 뉴스를 통해 투자기회를 찾고, 투자이익을 얻을 수 있을 것으로 기대한다. 그렇지만 너무나 많은 뉴스들이 실시간으로 생성·전파되며, 정작 어떤 뉴스가 중요한지, 뉴스가 주가에 미치는 영향은 얼마나 되는지를 알아내기는 쉽지 않다. 본 연구는 이러한 뉴스들을 수집·분석하여 주가와 어떠한 관련이 있는지 분석하였다. 뉴스는 그 속성상 특정한 양식을 갖지 않는 비정형 텍스트로 구성되어있다. 이러한 뉴스 컨텐츠를 분석하기 위해 오피니언 마이닝이라는 빅데이터 감성분석 기법을 적용하였고, 이를 통해 주가지수의 등락을 예측하는 지능형 투자의사결정 모형을 제시하였다. 그리고, 모형의 유효성을 검증하기 위하여 마이닝 결과와 주가지수 등락 간의 관계를 통계 분석하였다. 그 결과 뉴스 컨텐츠의 감성분석 결과값과 주가지수 등락과는 유의한 관계를 가지고 있었으며, 좀 더 세부적으로는 주식시장 개장 전 뉴스들과 주가지수의 등락과의 관계 또한 통계적으로 유의하여, 뉴스의 감성분석 결과를 이용해 주가지수의 변동성 예측이 가능할 것으로 판단되었다. 이렇게 도출된 투자의사결정 모형은 여러 유형의 뉴스 중에서 시황·전망·해외 뉴스가 주가지수변동을 가장 잘 예측하는 것으로 나타났고 로지스틱 회귀분석결과 분류정확도는 주가하락 시 70.0%, 주가상승 시 78.8%이며 전체평균은 74.6%로 나타났다.

웹사이트 개발을 위한 웹접근성 준수 프레임워크: - W 은행 인터넷 뱅킹 시스템 구축 사례 -

김유신 ( Yoosin Kim ),정승렬 ( Seung Ryul Jeong ) 한국인터넷정보학회 2013 인터넷정보학회논문지 Vol.14 No.5

인터넷의 발달과 함께 단편적인 HTML문서에 그쳤던 웹사이트가 방대한 콘텐츠와 서비스를 포함한 거대 웹 애플리케이션 시스템으로 확장되었다. 그러나 웹 서비스가 고도화될수록 웹 접근성이 저해되는 상황이 발생하였는데, 모바일/스마트 환경에서의 사용성이 미흡하고 장애인이나 노약자의 웹 이용에 불편과 차별이 발생하였기 때문이다. 이의 해소를 요구하는 장애인차별금지법이 2013년 4월 11일부로 모든 법인으로 확대됨에 따라 웹접근성을 확보하기 위한 웹사이트 개편이 붐을 이루고 있다. 그러나 금융거래나 전자상거래와 같이 복잡하고 다양한 기능과 솔루션으로 구성된 웹사이트에서 웹접근성을 준수하는 것은 결코 쉽지 않은 일이다. 은행의 경우 개편해야 할 콘텐츠의 양이 수만 페이지에 달하고, 디자인 이미지, HTML, 프로그래밍 소스, SW패키지 등 검토해야 할 웹접근성요소가 너무나 많고 복잡하다. 때문에 거대하고 고도화된 웹 애플리케이션과 서비스를 제공하는 웹사이트가 웹접근성을 확보하기 위해서는, 분석, 설계, 구현, 테스트 등 웹사이트 개발 전반에 걸쳐 웹접근성 준수를 지원할 수 있는 웹접근성 준수 프레임워크가 절실히 필요하다. 본 연구에서는 웹사이트 개발 방법론과 웹접근성 준수 표준 가이드, 웹사이트 특성에 따른 웹접근성 이슈 등을 종합적으로 검토하여 실무 적용이 가능한 웹접근성 준수 프레임워크를 제시하였다. 그리고 이를 W은행 인터넷뱅킹 재구축 프로젝트의 실제 사례에 적용하여 웹접근성 우수사이트 품질마크 인증이라는 프로젝트 목표를 달성함으로서 제시된 프레임워크의 의미와 가치를 확인할 수 있었다. As Internet advances, websites with simpel HTML pages are changing to complex web application systems with enormous contents and various services. With this trend, it is noted that situations where Web accessibility of the old and the handicapped is inhibited are growing. To solve this problem, The Disability Discrimination Act has been enacted since April 2013. This act triggers massive website reorganization efforts. However, in order for the huge and sophisticated web applications and web sites to ensure a web accessibility, a framework is required to throughout the web site development. Based on thorough review of website development methodologies, web accessibility compliance standards, and various web accessibility issues related to website characteristics, this study proposes a practice oriented “Web Accessibility Compliance Framework.” The current study also examines the usefulness and value of this framework by applying it to the internet banking development project of W bank and receiving a certificate for high quality website complying web accessibility standards.