• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.8
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• pp.3070-3075
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• 2010

Through Web 2.0 days, the end users express their opinions and thoughts for blogs and community spaces on the Internet. These opinions and thoughts are used to purchase products, however, users only refer to a few comments not overall opinions. Opinion Analysis System is an opinion search, developed from a natural language search, which analyzes the product's positive or negative evaluations using opinions of products and services on the Internet. In this paper, we suggest a syntactic analysis and inverse processing system that studies and processes 'Positive', 'Negative', 'Neutral' in addition to 'Inverse' information to analyze 'positive' or 'negative' for the core of sentences in Opinion Analysis Service.

• Structural Engineering and Mechanics
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• v.11 no.5
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• pp.485-502
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• 2001

Modem soft computing methods, such as neural networks, evolutionary models and fuzzy logic, are mainly inspired by the problem solving strategies the biological systems use in nature. As such, the soft computing methods are fundamentally different from the conventional engineering problem solving methods, which are based on mathematics. In the author's opinion, these fundamental differences are the key to the full understanding of the soft computing methods and in the realization of their full potential in engineering applications. The main theme of this paper is to discuss the fundamental differences between the soft computing methods and the mathematically based conventional methods in engineering problems, and to explore the potential of soft computing methods in new ways of formulating and solving the otherwise intractable engineering problems. Inverse problems are identified as a class of particularly difficult engineering problems, and the special capabilities of the soft computing methods in inverse problems are discussed. Soft computing methods are especially suited for engineering design, which can be considered as a special class of inverse problems. Several examples from the research work of the author and his co-workers are presented and discussed to illustrate the main points raised in this paper.

• 한국전산유체공학회:학술대회논문집
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• 2002.10a
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• pp.35-40
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• 2002

This paper deals with a thrust generation on flapping-airfoil by dynamic stall. Dynamic stall refers to a series of complicated aerodynamic phenomena accompanied by a stall delay in unsteady motion. In most cases, once it occurs, the dynamic stall may lead to an abrupt fluctuation of aerodynamic forces. An inverse $k\acute{a}rm\acute{a}n$ vortex has been considered as a main reason for a thrust generation. In this paper, however, we have found out that a thrust is closely related to reduced frequency and leading edge vortex in addition to inverse Karman vortex. In order to certify our opinion, picking and plunging motions were calculated with the parameter of amplitude and frequency by using the unsteady, incompressible Navier-Stokes flow solver with a two-equation turbulence model. For more efficient computation, it is parallelized by MPI programming method.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• Journal of the Korean Society of Systems Engineering
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• v.20 no.spc1
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• pp.12-29
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• 2024

The purpose of this study is to identify the social issues discussed in Korea regarding Maritime Autonomous Surface Ships (MASS), the most advanced ICT field in the shipbuilding industry, and to suggest policy implications. In recent years, it has become important to reflect social issues of public interest in the policymaking process. For this reason, an increasing number of studies use media data and social media to identify public opinion. In this study, we collected 2,843 domestic media articles related to MASS from 2017 to 2022, when MASS was officially discussed at the International Maritime Organization, and analyzed them using text mining techniques. Through term frequency-inverse document frequency (TF-IDF) analysis, major keywords such as 'shipbuilding,' 'shipping,' 'US,' and 'HD Hyundai' were derived. For LDA topic modeling, we selected eight topics with the highest coherence score (-2.2) and analyzed the main news for each topic. According to the combined analysis of five years, the topics '1. Technology integration of the shipbuilding industry' and '3. Shipping industry in the post-COVID-19 era' received the most media attention, each accounting for 16%. Conversely, the topic '5. MASS pilotage areas' received the least media attention, accounting for 8 percent. Based on the results of the study, the implications for policy, society, and international security are as follows. First, from a policy perspective, the government should consider the current situation of each industry sector and introduce MASS in stages and carefully, as they will affect the shipbuilding, port, and shipping industries, and a radical introduction may cause various adverse effects. Second, from a social perspective, while the positive aspects of MASS are often reported, there are also negative issues such as cybersecurity issues and the loss of seafarer jobs, which require institutional development and strategic commercialization timing. Third, from a security perspective, MASS are expected to change the paradigm of future maritime warfare, and South Korea is promoting the construction of a maritime unmanned system-based power, but it emphasizes the need for a clear plan and military leadership to secure and develop the technology. This study has academic and policy implications by shedding light on the multidimensional political and social issues of MASS through news data analysis, and suggesting implications from national, regional, strategic, and security perspectives beyond legal and institutional discussions.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.