Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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Multi-Category Sentiment Analysis for Social Opinion Related to Artificial Intelligence on Social Media

소셜 미디어 상에서의 인공지능 관련 사회적 여론에 대한 다 범주 감성 분석

  • Lee, Sang Won (Dept. of Business Informatics, Graduate School, Hanyang University) ;
  • Choi, Chang Wook (Dept. of Business Informatics, Graduate School, Hanyang University) ;
  • Kim, Dong Sung (Dept. of Business Administration, Graduate School, Hanyang University) ;
  • Yeo, Woon Young (Dept. of Business Informatics, Graduate School, Hanyang University) ;
  • Kim, Jong Woo (School of Business, Hanyang University)
  • 이상원 (한양대학교 일반대학원 비즈니스인포매틱스학과) ;
  • 최창욱 (한양대학교 일반대학원 비즈니스인포매틱스학과) ;
  • 김동성 (한양대학교 일반대학원 경영학과) ;
  • 여운영 (한양대학교 일반대학원 비즈니스인포매틱스학과) ;
  • 김종우 (한양대학교 경영대학 경영학부)
  • Received : 2018.05.29
  • Accepted : 2018.10.24
  • Published : 2018.12.31
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Abstract

As AI (Artificial Intelligence) technologies have been swiftly evolved, a lot of products and services are under development in various fields for better users' experience. On this technology advance, negative effects of AI technologies also have been discussed actively while there exists positive expectation on them at the same time. For instance, many social issues such as trolley dilemma and system security issues are being debated, whereas autonomous vehicles based on artificial intelligence have had attention in terms of stability increase. Therefore, it needs to check and analyse major social issues on artificial intelligence for their development and societal acceptance. In this paper, multi-categorical sentiment analysis is conducted over online public opinion on artificial intelligence after identifying the trending topics related to artificial intelligence for two years from January 2016 to December 2017, which include the event, match between Lee Sedol and AlphaGo. Using the largest web portal in South Korea, online news, news headlines and news comments were crawled. Considering the importance of trending topics, online public opinion was analysed into seven multiple sentimental categories comprised of anger, dislike, fear, happiness, neutrality, sadness, and surprise by topics, not only two simple positive or negative sentiment. As a result, it was found that the top sentiment is "happiness" in most events and yet sentiments on each keyword are different. In addition, when the research period was divided into four periods, the first half of 2016, the second half of the year, the first half of 2017, and the second half of the year, it is confirmed that the sentiment of 'anger' decreases as goes by time. Based on the results of this analysis, it is possible to grasp various topics and trends currently discussed on artificial intelligence, and it can be used to prepare countermeasures. We hope that we can improve to measure public opinion more precisely in the future by integrating empathy level of news comments.

인공지능 기술의 비약적인 발전으로 인하여, 사용자의 편의성 증대를 목적으로 다양한 분야에서 관련된 제품과 서비스들의 개발이 이루어지고 있다. 이러한 기술의 발전에는 긍정적인 파급 효과에 대한 기대감이 존재하나, 향후 발생 가능한 부정적인 측면에 대한 논의도 활발히 이루어지고 있다. 예를 들어, 인공지능 기술 기반의 자율주행 자동차의 경우 안정성의 향상이라는 측면에서 많은 관심을 받고 있으나, 트롤리 딜레마, 시스템 보안 문제 등의 사회적 이슈 또한 활발히 논의되고 있다. 이에 따라, 인공지능 관련 기술의 발전과 사회적 수용을 위해서는 사회적으로 논의되는 주요 관련 이슈들에 대한 확인과 효과적인 분석이 요구된다. 이를 위해, 본 연구에서는 '이세돌 vs 알파고' 시점인 2016년 3월을 포함하여 2016년 1월부터 2017년 12월까지 2년 동안의 인공지능과 관련된 사회적인 이슈들을 파악하고 온라인상에서 발생되는 사회적 여론에 대하여 다 범주 감성을 분석하고자 한다. 이를 위하여 국내 대표적인 포털 사이트에서 인공지능 관련 뉴스의 수와 관련된 뉴스 제목, 뉴스의 댓글을 웹 크롤링(Web Crawling) 하였다. 사회적 여론에 대한 다 범주 감성 분석은 논의되는 이슈들의 중요성을 고려하여 단순 긍정 또는 부정이 아닌, 분노, 혐오, 두려움, 행복, 중립, 슬픔, 놀라움의 7가지 다 범주 감성으로 분석하였다. 분석 결과, 대부분의 이벤트 기간에 대하여 1위 감성은 '행복'으로 나타났지만 각 키워드에 대하여 나오는 감성이 상이함을 볼 수 있었다. 또한 2016년 상반기, 하반기, 2017년 상반기, 하반기로 나누어 보았을 때 시간이 지남에 따라 '분노'의 감성이 낮아짐을 확인하였다. 이러한 분석 결과를 바탕으로 인공지능과 관련하여 현재 논의되고 있는 다양한 이슈와 동향 파악이 가능하며, 이에 대한 대응 방안 마련에 활용이 가능할 것이다. 향후 감성 분석기의 성능 향상과 댓글에 대한 공감 및 비공감도의 가중치를 추가하여 분석한다면 사회적 여론을 보다 세밀하게 파악 할 수 있을 것이다.

Keywords

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Research Process

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Sentiment distribution of 28 January 2016

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Sentiment distribution from March 7 to 17, 2016

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Sentiment distribution of 31 August 2016

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Sentiment distribution of 15 December 2016

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Sentiment distribution of 29 December 2016

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Sentiment distribution of 6 January 2017

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Sentiment distribution of 9 January 2017

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Sentiment distribution from January 16 to 18, 2017

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Sentiment distribution from March 29 to 30, 2017

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Sentiment distribution of 11 October 2017

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Sentiment distribution of 31 October 2017

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Ratio of the top four sentiment by half

Extract keywords based on event period

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News comment and sentiment (31 October 2017)

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The frequency and ratio of sentiment in news comments (31 October 2017)

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