• Informatization Policy
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• v.27 no.2
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• pp.66-83
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• 2020

Local issues that occur in cities typically garner great attention from the public. While local governments strive to resolve these issues, it is often difficult to effectively eliminate them all, which leads to complaints. In tackling these issues, it is imperative for local governments to use big data to identify the nature of complaints, and proactively provide solutions. This study applies the LDA topic modeling technique to research and analyze trends and patterns in complaints filed online. To this end, 9,625 cases of online complaints submitted to the city of Busan from 2015 to 2017 were analyzed, and 20 topics were identified. From these topics, key topics were singled out, and through analysis of quarterly weighting trends, four "hot" topics(Bus stops, Taxi drivers, Praises, and Administrative handling) and four "cold" topics(CCTV installation, Bus routes, Park facilities including parking, and Festivities issues) were highlighted. The study conducted big data analysis for the identification of trends and patterns in civil affairs and makes an academic impact by encouraging follow-up research. Moreover, the text mining technique used for complaint analysis can be used for other projects requiring big data processing.

• Journal of the Korean Society for information Management
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• v.35 no.3
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• pp.77-100
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• 2018

The objectives of this study is to present a discovering process of interdisciplinary convergence technology using text mining of big data. For the convergence research of biotechnology(BT) and information communications technology (ICT), the following processes were performed. (1) Collecting sufficient meta data of research articles based on BT terminology list. (2) Generating intellectual structure of emerging technologies by using a Pathfinder network scaling algorithm. (3) Analyzing contents with topic modeling. Next three steps were also used to derive items of BT-ICT convergence technology. (4) Expanding BT terminology list into superior concepts of technology to obtain ICT-related information from BT. (5) Automatically collecting meta data of research articles of two fields by using OpenAPI service. (6) Analyzing contents of BT-ICT topic models. Our study proclaims the following findings. Firstly, terminology list can be an important knowledge base for discovering convergence technologies. Secondly, the analysis of a large quantity of literature requires text mining that facilitates the analysis by reducing the dimension of the data. The methodology we suggest here to process and analyze data is efficient to discover technologies with high possibility of interdisciplinary convergence.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.625-637
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• 2020

In order to decide the naval ship weapon system acquisition for national policy/market economy activities, the decision makers can determine policy based on current technology level/concentration/utilization. For this, the decision makers apply the major common technology field analysis using patents data. As a method for collecting patent data, we can collect patent data of domestic mobile carriers through the Korea Intellectual Property Rights Information System of Korean Intellectual Property Office. As a result, we collected 14,964 patents/352 International Patent Classification(IPC) types. Based on these data, we performed three analysis processes (SNA, PCA, ARIMA, Text Mining) and got each result from extracting 58 IPC types of SNA and 7 IPC types of PCA. Based on the analysis results, we have confirmed that 7 IPC(B63B, H01M, F03D, B01D, H02K, B23K, H01H) types are the Major Common Technology Distribution of domestic Defense Companies.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.283-303
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• 2007

Recently, the study of efficient way to store and retrieve enormous music data is becoming the one of important issues in the multimedia database. Most general method of MIR (Music Information Retrieval) includes a text-based approach using text information to search a desired music. However, if users did not remember the keyword about the music, it can not give them correct answers. Moreover, since these types of systems are implemented only for exact matching between the query and music data, it can not mine any information on similar music data. Thus, these systems are inappropriate to achieve similarity matching of music data. In order to solve the problem, we propose an Efficient Query-By-Humming System (EQBHS) with a content-based indexing method that efficiently retrieve and store music when a user inquires with his incorrect humming. For the purpose of accelerating query processing in EQBHS, we design indices for significant melodies, which are 1) frequent melodies occurring many times in a single music, on the assumption that users are to hum what they can easily remember and 2) melodies partitioned by rests. In addition, we propose an error tolerated mapping method from a note to a character to make searching efficient, and the frequent melody extraction algorithm. We verified the assumption for frequent melodies by making up questions and compared the performance of the proposed EQBHS with N-gram by executing various experiments with a number of music data.

• The Journal of Society for e-Business Studies
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• v.22 no.1
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• pp.123-146
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• 2017

The information that consumers share in the SNS (Social Networking Service) has a great influence on the purchase of consumers. Therefore, it is necessary to pay attention to new research methodology and advertising strategy using Social Big Data. In this context, the purpose of this study is to quantitatively analyze customer value through Social Big Data. In this study, we analyzed the value structure of consumers for the three smartphone brands through text mining and positive/negative image analysis. Analysis result, it was possible to distinguish the emotional aspects (sensitivity) and rational aspects (rationality) for customer value per brand. In the case of the Galaxy S7 and iPhone 6S, emotional aspects were important before the launch, but the rational aspects was important after release date. On the other hand, in the case of the LG G5, emotional aspects were important before and after launch. We can propose two core advertising strategies based on analyzed consumer value. When developing advertising strategy in the case of the Galaxy S7, there is a need to emphasize the rational aspects of product attributes and differentiated functions. In the case of the LG G5, it is necessary to consider the emotional aspects of happiness, excitement, pleasure, and fun that are felt by using products in advertising strategy. As a result, this study will provide a good standard for actual advertising strategy through consumer value analysis. Advertising strategies are primarily driven by intuition or experience. Therefore, it is important to develop advertising strategies by analyzing consumer value through social big data analysis.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.7
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• pp.221-228
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• 2020

Part-of-Speech (POS) tagged corpus is a collection of electronic text in which each word is annotated with a tag as the corresponding POS and is widely used for various training data for natural language processing. The training data generally assumes that there are no errors, but in reality they include various types of errors, which cause performance degradation of systems trained using the data. To alleviate this problem, we propose a novel method for detecting errors in the existing POS tagged corpus using the classifier of XGBoost and cross-validation as evaluation techniques. We first train a classifier of a POS tagger using the POS-tagged corpus with some errors and then detect errors from the POS-tagged corpus using cross-validation, but the classifier cannot detect errors because there is no training data for detecting POS tagged errors. We thus detect errors by comparing the outputs (probabilities of POS) of the classifier, adjusting hyperparameters. The hyperparameters is estimated by a small scale error-tagged corpus, in which text is sampled from a POS-tagged corpus and which is marked up POS errors by experts. In this paper, we use recall and precision as evaluation metrics which are widely used in information retrieval. We have shown that the proposed method is valid by comparing two distributions of the sample (the error-tagged corpus) and the population (the POS-tagged corpus) because all detected errors cannot be checked. In the near future, we will apply the proposed method to a dependency tree-tagged corpus and a semantic role tagged corpus.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.798-809
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• 2020

Evidence-based policymaking and assessments for scientific administration have increased the importance of statistics (data) utilization. Statistics can explain specific phenomena by providing numerical values and are a public resource for national decision making. Due to these inherent attributes, statistics are utilized as baseline and base data for government policy determinations and the analysis of various phenomena. However, compared to the importance, the role of statistics is limited, and statistics are often used as simple abstracts, produced mainly for suppliers, not for consumers' perspectives to create value. This study explores the statistical data and other attributes that can be utilized for policies or research to address the problems mentioned above. The baseline statistical data used in this study is from the Maritime Distress Accident Statistical Yearbook published by the South Korean Coast Guard, and other additional attributes are from text analyses of vessel casualty situation reports from the South Korean Maritime Police. Collecting 56 attributes drawn from the text analysis and executing an EDA resulted in 88 attribute unions: 18 attribute unions had a satisfactory significance probability (p-value < .05) and a strong correlation coefficient above 0.7, and 70 attribute unions had a middle correlation. (over 0.4 and under 0.7). Additionally, to utilize the extra attributes discovered from the EDA politically, a keyword analysis for each detailed strategy of the disaster Preparation basic plan was executed, the utilization availability of the attributes was obtained using a matching process of keywords, and the EDA deducted attributes were examined.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.27-35
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• 2022

The purpose of this study is to analyze the metaverse-related issues in the news big data from an educational perspective, explore their characteristics, and provide implications for the educational applicability of the metaverse and future education. To this end, 41,366 cases of metaverse-related data searched on portal sites were collected, and weight values of all extracted keywords were calculated and ranked using TF-IDF, a representative term weight model, and then word cloud visualization analysis was performed. In addition, major topics were analyzed using topic modeling(LDA), a sophisticated probability-based text mining technique. As a result of the study, topics such as platform industry, future talent, and extension in technology were derived as core issues of the metaverse from an educational perspective. In addition, as a result of performing secondary data analysis under three key themes of technology, job, and education, it was found that metaverse has issues related to education platform innovation, future job innovation, and future competency innovation in future education. This study is meaningful in that it analyzes a vast amount of news big data in stages to draw issues from an education perspective and provide implications for future education.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of the Korea Institute of Building Construction
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• v.24 no.1
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• pp.157-168
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• 2024

The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.