• Title/Summary/Keyword: Search Keyword Extraction

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An Analysis of Global Gamification Cases in CS Education (해외 CS 교육 게이미피케이션 사례 분석)

  • Kang, Seungheon;Park, Sungjin;Kim, Sangkyun
    • Journal of Korea Game Society
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    • v.17 no.6
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    • pp.39-50
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    • 2017
  • Gamification in education widely used in the field of computer science (CS). The purpose of this study is to analyze the pre-studies of gamification cases in CS education and suggest the direction of CS education. For the study, an empirical research method was applied. Through keyword search, 1220 pre-studies were collected, among which 55 were compressed through the first and second extraction processes. In the results section, this study summarized the presented year, applied subject, subject of education, results and limitations. Additional study not only will analyze gamification of CS Education and compare with this study's results, but it will suggest the development direction of gamification in CS education in Korea.

Relevance Feedback Agent for Improving Precision in Korean Web Information Retrieval System (한국어 웹 정보검색 시스템의 정확도 향상을 위한 연관 피드백 에이전트)

  • Baek, Jun-Ho;Choe, Jun-Hyeok;Lee, Jeong-Hyeon
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.7
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    • pp.1832-1840
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    • 1999
  • Since the existed Korean Web IR systems generally use boolean system, it is difficult to retrieve the information to be wanted at one time. Also, because of the feature that web documents have the frequent abbreviation and many links, the keyword extraction using the inverted document frequency extracts the improper keywords for adding ambiguous meaning problem. Therefore, users must repeat the modification of the queries until they get the proper information. In this paper, we design and implement the relevance feedback agent system for resolving the above problems. The relevance feedback agent system extracts the proper information in response to user's preferred keywords and stores these keywords in preference DB table. When users retrieve this information later, the relevance feedback agent system will search it adding relevant keywords to user's queries. As a result of this method, the system can reduce the number of modification of user's queries and improve the efficiency of the IR system.

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A study on the efficient extraction method of SNS data related to crime risk factor (범죄발생 위험요소와 연관된 SNS 데이터의 효율적 추출 방법에 관한 연구)

  • Lee, Jong-Hoon;Song, Ki-Sung;Kang, Jin-A;Hwang, Jung-Rae
    • Journal of the Korea Society of Computer and Information
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    • v.20 no.1
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    • pp.255-263
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    • 2015
  • In this paper, we suggest a plan to take advantage of the SNS data to proactively identify the information on crime risk factor and to prevent crime. Recently, SNS(Social Network Service) data have been used to build a proactive prevention system in a variety of fields. However, when users are collecting SNS data with simple keyword, the result is contain a large amount of unrelated data. It may possibly accuracy decreases and lead to confusion in the data analysis. So we present a method that can be efficiently extracted by improving the search accuracy through text mining analysis of SNS data.

A Study on the Keyword Extraction for ESG Controversies Through Association Rule Mining (연관규칙 분석을 통한 ESG 우려사안 키워드 도출에 관한 연구)

  • Ahn, Tae Wook;Lee, Hee Seung;Yi, June Suh
    • The Journal of Information Systems
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    • v.30 no.1
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    • pp.123-149
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    • 2021
  • Purpose The purpose of this study is to define the anti-ESG activities of companies recognized by media by reflecting ESG recently attracted attention. This study extracts keywords for ESG controversies through association rule mining. Design/methodology/approach A research framework is designed to extract keywords for ESG controversies as follows: 1) From DeepSearch DB, we collect 23,837 articles on anti-ESG activities exposed to 130 media from 2013 to 2018 of 294 listed companies with ESG ratings 2) We set keywords related to environment, social, and governance, and delete or merge them with other keywords based on the support, confidence, and lift derived from association rule mining. 3) We illustrate the importance of keywords and the relevance between keywords through density, degree centrality, and closeness centrality on network analysis. Findings We identify a total of 26 keywords for ESG controversies. 'Gapjil' records the highest frequency, followed by 'corruption', 'bribery', and 'collusion'. Out of the 26 keywords, 16 are related to governance, 8 to social, and 2 to environment. The keywords ranked high are mostly related to the responsibility of shareholders within corporate governance. ESG controversies associated with social issues are often related to unfair trade. As a result of confidence analysis, the keywords related to social and governance are clustered and the probability of mutual occurrence between keywords is high within each group. In particular, in the case of "owner's arrest", it is caused by "bribery" and "misappropriation" with an 80% confidence level. The result of network analysis shows that 'corruption' is located in the center, which is the most likely to occur alone, and is highly related to 'breach of duty', 'embezzlement', and 'bribery'.

A Design of Similar Video Recommendation System using Extracted Words in Big Data Cluster (빅데이터 클러스터에서의 추출된 형태소를 이용한 유사 동영상 추천 시스템 설계)

  • Lee, Hyun-Sup;Kim, Jindeog
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.2
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    • pp.172-178
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    • 2020
  • In order to recommend contents, the company generally uses collaborative filtering that takes into account both user preferences and video (item) similarities. Such services are primarily intended to facilitate user convenience by leveraging personal preferences such as user search keywords and viewing time. It will also be ranked around the keywords specified in the video. However, there is a limit to analyzing video similarities using limited keywords. In such cases, the problem becomes serious if the specified keyword does not properly reflect the item. In this paper, I would like to propose a system that identifies the characteristics of a video as it is by the system without human intervention, and analyzes and recommends similarities between videos. The proposed system analyzes similarities by taking into account all words (keywords) that have different meanings from training videos, and in such cases, the methods handled by big data clusters are applied because of the large scale of data and operations.

Analyzing Media Bias in News Articles Using RNN and CNN (순환 신경망과 합성곱 신경망을 이용한 뉴스 기사 편향도 분석)

  • Oh, Seungbin;Kim, Hyunmin;Kim, Seungjae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.8
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    • pp.999-1005
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    • 2020
  • While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.

Group-wise Keyword Extraction of the External Audit using Text Mining and Association Rules (텍스트마이닝과 연관규칙을 이용한 외부감사 실시내용의 그룹별 핵심어 추출)

  • Seong, Yoonseok;Lee, Donghee;Jung, Uk
    • Journal of Korean Society for Quality Management
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    • v.50 no.1
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    • pp.77-89
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    • 2022
  • Purpose: In order to improve the audit quality of a company, an in-depth analysis is required to categorize the audit report in the form of a text document containing the details of the external audit. This study introduces a systematic methodology to extract keywords for each group that determines the differences between groups such as 'audit plan' and 'interim audit' using audit reports collected in the form of text documents. Methods: The first step of the proposed methodology is to preprocess the document through text mining. In the second step, the documents are classified into groups using machine learning techniques and based on this, important vocabularies that have a dominant influence on the performance of classification are extracted. In the third step, the association rules for each group's documents are found. In the last step, the final keywords for each group representing the characteristics of each group are extracted by comparing the important vocabulary for classification with the important vocabulary representing the association rules of each group. Results: This study quantitatively calculates the importance value of the vocabulary used in the audit report based on machine learning rather than the qualitative research method such as the existing literature search, expert evaluation, and Delphi technique. From the case study of this study, it was found that the extracted keywords describe the characteristics of each group well. Conclusion: This study is meaningful in that it has laid the foundation for quantitatively conducting follow-up studies related to key vocabulary in each stage of auditing.

Anatomy of Sentiment Analysis of Tweets Using Machine Learning Approach

  • Misbah Iram;Saif Ur Rehman;Shafaq Shahid;Sayeda Ambreen Mehmood
    • International Journal of Computer Science & Network Security
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    • v.23 no.10
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    • pp.97-106
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    • 2023
  • Sentiment analysis using social network platforms such as Twitter has achieved tremendous results. Twitter is an online social networking site that contains a rich amount of data. The platform is known as an information channel corresponding to different sites and categories. Tweets are most often publicly accessible with very few limitations and security options available. Twitter also has powerful tools to enhance the utility of Twitter and a powerful search system to make publicly accessible the recently posted tweets by keyword. As popular social media, Twitter has the potential for interconnectivity of information, reviews, updates, and all of which is important to engage the targeted population. In this work, numerous methods that perform a classification of tweet sentiment in Twitter is discussed. There has been a lot of work in the field of sentiment analysis of Twitter data. This study provides a comprehensive analysis of the most standard and widely applicable techniques for opinion mining that are based on machine learning and lexicon-based along with their metrics. The proposed work is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous, and polarized positive, negative or neutral. In order to validate the performance of the proposed framework, an extensive series of experiments has been performed on the real world twitter dataset that alter to show the effectiveness of the proposed framework. This research effort also highlighted the recent challenges in the field of sentiment analysis along with the future scope of the proposed work.

Trends in the Use of Artificial Intelligence in Medical Image Analysis (의료영상 분석에서 인공지능 이용 동향)

  • Lee, Gil-Jae;Lee, Tae-Soo
    • Journal of the Korean Society of Radiology
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    • v.16 no.4
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    • pp.453-462
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    • 2022
  • In this paper, the artificial intelligence (AI) technology used in the medical image analysis field was analyzed through a literature review. Literature searches were conducted on PubMed, ResearchGate, Google and Cochrane Review using the key word. Through literature search, 114 abstracts were searched, and 98 abstracts were reviewed, excluding 16 duplicates. In the reviewed literature, AI is applied in classification, localization, disease detection, disease segmentation, and fit degree of registration images. In machine learning (ML), prior feature extraction and inputting the extracted feature values into the neural network have disappeared. Instead, it appears that the neural network is changing to a deep learning (DL) method with multiple hidden layers. The reason is thought to be that feature extraction is processed in the DL process due to the increase in the amount of memory of the computer, the improvement of the calculation speed, and the construction of big data. In order to apply the analysis of medical images using AI to medical care, the role of physicians is important. Physicians must be able to interpret and analyze the predictions of AI algorithms. Additional medical education and professional development for existing physicians is needed to understand AI. Also, it seems that a revised curriculum for learners in medical school is needed.

Intelligent Web Crawler for Supporting Big Data Analysis Services (빅데이터 분석 서비스 지원을 위한 지능형 웹 크롤러)

  • Seo, Dongmin;Jung, Hanmin
    • The Journal of the Korea Contents Association
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    • v.13 no.12
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    • pp.575-584
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    • 2013
  • Data types used for big-data analysis are very widely, such as news, blog, SNS, papers, patents, sensed data, and etc. Particularly, the utilization of web documents offering reliable data in real time is increasing gradually. And web crawlers that collect web documents automatically have grown in importance because big-data is being used in many different fields and web data are growing exponentially every year. However, existing web crawlers can't collect whole web documents in a web site because existing web crawlers collect web documents with only URLs included in web documents collected in some web sites. Also, existing web crawlers can collect web documents collected by other web crawlers already because information about web documents collected in each web crawler isn't efficiently managed between web crawlers. Therefore, this paper proposed a distributed web crawler. To resolve the problems of existing web crawler, the proposed web crawler collects web documents by RSS of each web site and Google search API. And the web crawler provides fast crawling performance by a client-server model based on RMI and NIO that minimize network traffic. Furthermore, the web crawler extracts core content from a web document by a keyword similarity comparison on tags included in a web documents. Finally, to verify the superiority of our web crawler, we compare our web crawler with existing web crawlers in various experiments.