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텍스트 분석 기술 및 활용 동향
Investigations on Techniques and Applications of Text Analytics 원문보기

한국통신학회논문지 = The Journal of Korean Institute of Communications and Information Sciences, v.42 no.2, 2017년, pp.471 - 492  

김남규 (Kookmin University School of MIS) ,  이동훈 (Kookmin University The Graduate School of Business Information Technology) ,  최호창 (Kookmin University The Graduate School of Business Information Technology)

초록
AI-Helper 아이콘AI-Helper

최근 데이터의 양 자체가 해결해야 할 문제의 일부분이 되는 빅데이터(Big Data) 분석에 대한 수요와 관심이 급증하고 있다. 빅데이터는 기존의 정형 데이터 뿐 아니라 이미지, 동영상, 로그 등 다양한 형태의 비정형 데이터 또한 포함하는 개념으로 사용되고 있으며, 다양한 유형의 데이터 중 특히 정보의 표현 및 전달을 위한 대표적 수단인 텍스트(Text) 분석에 대한 연구가 활발하게 이루어지고 있다. 텍스트 분석은 일반적으로 문서 수집, 파싱(Parsing) 및 필터링(Filtering), 구조화, 빈도 분석 및 유사도 분석의 순서로 수행되며, 분석의 결과는 워드 클라우드(Word Cloud), 워드 네트워크(Word Network), 토픽 모델링(Topic Modeling), 문서 분류, 감성 분석 등의 형태로 나타나게 된다. 특히 최근 다양한 소셜미디어(Social Media)를 통해 급증하고 있는 텍스트 데이터로부터 주요 토픽을 파악하기 위한 수요가 증가함에 따라, 방대한 양의 비정형 텍스트 문서로부터 주요 토픽을 추출하고 각 토픽별 해당 문서를 묶어서 제공하는 토픽 모델링에 대한 연구 및 적용 사례가 다양한 분야에서 생성되고 있다. 이에 본 논문에서는 텍스트 분석 관련 주요 기술 및 연구 동향을 살펴보고, 토픽 모델링을 활용하여 다양한 분야의 문제를 해결한 연구 사례를 소개한다.

Abstract ▼ AI-Helper 아이콘AI-Helper

The demand and interest in big data analytics are increasing rapidly. The concepts around big data include not only existing structured data, but also various kinds of unstructured data such as text, images, videos, and logs. Among the various types of unstructured data, text data have gained partic...

주제어

#Big Data   #Data Mining   #Text Analytics   #Topic Modeling  

질의응답

핵심어 질문 논문에서 추출한 답변
텍스트 분석은 어떤 순서로 수행되는가? 텍스트 분석은 일반적으로 문서 수집, 파싱(Parsing) 및 필터링(Filtering), 구조화, 빈도 분석 및 유사도 분석의 순서로 수행되며, 텍스트 분석 관련 기술은 그림 1과 같이 크게 비정형 텍스트의 구조화까지의 단계와 구조화된 문서의 분석 및 활용의 두 단계로 구분하여 살펴볼 수 있다. 일부 연구에서는 논문, 특허, 연구보고서 등 분석 대상인 텍스트 문서가 데이터베이스 형태로 제공되기도 하지만, 대부분의 연구에서 텍스트 문서는 크롤링을 통해 직접 수집하는 경우가 많다.
빅데이터란 무엇인가? 최근 다양한 분야에서 빅데이터(Big Data), 빅데이터 분석, 그리고 빅데이터 활용에 대한 관심이 학계와 업계를 불문하고 급증하고 있다. 빅데이터는 데이터의 양이 너무 방대해서 기존의 방법이나 도구로는 수집, 저장, 검색, 분석, 그리고 시각화가 어려운 데이터를 의미하며, 빅데이터 분석의 경우 데이터의 양 자체가 해결해야 할 문제의 일부분이 된다. 빅데이터의 특징으로는 주로 데이터의 양(Volume), 데이터 입출력의 속도(Velocity), 그리고 데이터 종류의 다양성 (Variety)을 나타내는 3V의 개념이 소개되고 있다.
질의응답 정보가 도움이 되었나요?

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