The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Complaints of Non-Face-to-Face Work before and during COVID-19 by Using Topic Modeling and Sentiment Analysis

동적 토픽 모델링과 감성 분석을 이용한 COVID-19 구간별 비대면 근무 부정요인 검출에 관한 연구

  • 이선민 (연세대학교 산업공학과) ;
  • 천세진 (동아대학교 컴퓨터공학과) ;
  • 박상언 (경기대학교 경영정보전공) ;
  • 이태욱 (연세대학교 산업공학과) ;
  • 김우주 (연세대학교 산업공학과)
  • Received : 2021.12.01
  • Accepted : 2021.12.20
  • Published : 2021.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Purpose The purpose of this study is to analyze the sentiment responses of the general public to non-face-to-face work using text mining methodology. As the number of non-face-to-face complaints is increasing over time, it is difficult to review and analyze in traditional methods such as surveys, and there is a limit to reflect real-time issues. Approach This study has proposed a method of the research model, first by collecting and cleansing the data related to non-face-to-face work among tweets posted on Twitter. Second, topics and keywords are extracted from tweets using LDA(Latent Dirichlet Allocation), a topic modeling technique, and changes for each section are analyzed through DTM(Dynamic Topic Modeling). Third, the complaints of non-face-to-face work are analyzed through the classification of positive and negative polarity in the COVID-19 section. Findings As a result of analyzing 1.54 million tweets related to non-face-to-face work, the number of IDs using non-face-to-face work-related words increased 7.2 times and the number of tweets increased 4.8 times after COVID-19. The top frequently used words related to non-face-to-face work appeared in the order of remote jobs, cybersecurity, technical jobs, productivity, and software. The words that have increased after the COVID-19 were concerned about lockdown and dismissal, and business transformation and also mentioned as to secure business continuity and virtual workplace. New Normal was newly mentioned as a new standard. Negative opinions found to be increased in the early stages of COVID-19 from 34% to 43%, and then stabilized again to 36% through non-face-to-face work sentiment analysis. The complaints were, policies such as strengthening cybersecurity, activating communication to improve work productivity, and diversifying work spaces.

Keywords

References

  1. 강재정, "원격근무환경에서 일과 삶의 균형방안: 인과지도, 사고프로세스와 트리즈의 적용", 한국정보시스템학회, 정보시스템연구, 29권 3호, 2020, pp. 179-195
  2. 강찬희, "동적 토픽 모델링과 의미연결망을 통한 코로나19 이슈분석", 국내 석사학위논문, 성균관대학교 일반대학원, 서울, 2021
  3. 고은지, 최선영, "트윗의 타임 시퀀스를 활용한 DTM 분석 : 2019 남북미정상회동 이벤트를 중심으로", 한국정보통신학회논문지, 25(2), 2021, pp. 308-313 https://doi.org/10.6109/JKIICE.2021.25.2.308
  4. 박일경, "재택형 유연근무제의 실태에 관한 연구", 2019, 국내 석사학위논문, 인천대학교, 인천
  5. 서창적, 김효정, "델파이 기법을 이용한 비대면 서비스 재택근무 적합요인의 도출", 서비스경영학회지 14.1, 2013, pp. 207-227
  6. 우창우, 이종연, "다이나믹 토픽 모델을 활용한 D(Data).N(Network).A(A.I) 중심의 연구동향 분석", 한국융합학회논문지, 11(9), 2020, pp. 21-29 https://doi.org/10.15207/JKCS.2020.11.9.021
  7. 이종화, "SNS대상의 지능형 자연어 수집, 처리 시스템 구현을 통한 한국형 감성사전 구축에 관한 연구", 한국정보시스템학회, 정보시스템연구 29권 3호, 2020, pp. 237-251
  8. 이주양, "소셜 미디어 감성 분석을 활용한 항공사 고객만족도 연구 : 한, 영, 일, 중 언어권을 중심으로", 관광경영연구, 24(6), 2020, pp. 527-546
  9. 이진영, "스마트워크가 자기효능감과 업무창의성 및 조직성과에 미치는 구조관계 연구", 국내 박사학위논문 경희대학교 대학원, 서울, 2020
  10. 이혜진, 최자영, "텍스트 마이닝 기법을 활용한 편의점 이용 고객의 트위터 리뷰에 대한 감성 분석", 글로벌 경영학회지, 2019, pp. 143-164
  11. 정용주, 김진수, "재택근무 제도강화와 테크노스트레스가 업무생산성에 미치는 영향에 관한 연구 : 행동통제와 기술준비도의 조절효과를 중심으로", JITAM, 27(4), 2020, pp. 63-83
  12. 차영란, "코로나 이전과 이후의 4차 산업혁명과 광고의 뉴스기사 분석 : LDA와 Word2vec을 중심으로", 한국콘텐츠학회논문지 제21권 제9호, 2021, pp. 149- 163 https://doi.org/10.5392/JKCA.2021.21.09.149
  13. 최홍열, 박은경, "소셜 미디어 빅데이터 분석을 이용한 나홀로 여행 트렌드 분석 : 제주도를 중심으로", 관광경영연구, 23(1), 2019, pp. 45-64
  14. Alec Go, Richa Bhayani, Lei Huang, "Twitter Sentiment Classification Using Distant Supervision", CS224N Project Report, Stanford, 2009, pp. 12
  15. Andrea Alexander, Rich Cracknell, Aaron De Smet, Meredith Langstaff, Mihir Mysore, and Dan Ravid, "What Executives Are Saying about the Future of Hybrid Work", McKinsey Insights, 2021
  16. Andrew N. O'Connor, "Probability Distributions Used in Reliability Engineering", Center for Risk and Reliability, 2011
  17. Bondarenko Olena M., Tardaskina Tetiana M., Bogatyryova Lyudmyla D., "Researching the Development of Remote Work in the Context of the COVID-19 Pandemic", BIZNES INFORM, Vol. 2, no. 517, 2021, pp. 190-197
  18. Bo Pang, Lillian Lee, "Opinion Mining and Sentiment Analysis", Boston : Now Publishers, 2008, pp. 1-135
  19. Changqin Quan, Fuji Ren, "Unsupervised Product Feature Extraction for Feature-Oriented Opinion Determination", Information Sciences, Vol. 272, 2014, pp. 16-28 https://doi.org/10.1016/j.ins.2014.02.063
  20. David M. Blei, "Latent Dirichlet Allocation", Journal of machine learning research, ISSN : 1532-4435, 2003, pp. 993-1022
  21. Farah Shahid, Aneela Zameer, Muhammad Muneeb, "Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM", Chaos, Solitons & Fractals, Vol. 140, 2020
  22. Fu Xianghua, Liu Guo, Guo Yanyan, Wang Zhiqiang, "Multi-Aspect Sentiment Analysis for Chinese Online Social Reviews Based on Topic Modeling and HowNet Lexicon", Knowledge-Based Systems, Vol. 37, 2013, pp. 186-195 https://doi.org/10.1016/j.knosys.2012.08.003
  23. Jeremy Barnes, Roman Klinger, Sabine Schulte Im Walde, "Assessing State-of-the-Art Sentiment Models on State-of-the-Art Sentiment Datasets", In Proceedings of WASSA, 2017, pp. 2-12
  24. J. Grimmer, "A Bayesian Hierarchical Topic Model for Political Texts : Measuring Expressed Agendas in Senate Press Releases", Political Analysis, 18, 2010, pp. 1-35 https://doi.org/10.1093/pan/mpp034
  25. Jian Jin, Ping Ji, Rui Gu, "Identifying Comparative Customer Requirements from Product Online Reviews for Competitor Analysis", Engineering Applications of Artificial Intelligence, Vol. 49, 2015, pp. 61-73 https://doi.org/10.1016/j.engappai.2015.12.005
  26. Kasper Christensen, Kristian Hovde Liland, Knut Kvaal, Einar Risvik, Alessandra Biancolillo, Joachim Scholderer, Sladjana Norskov, Tormod Naes, "Mining Online Community Data : The Nature of Ideas in Online Communities", Food Quality and Preference, Vol. 62, 2017, pp. 246-256 https://doi.org/10.1016/j.foodqual.2017.06.001
  27. Mathieu Cliche, "BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs", Proceedings of SemEval, 2017, pp. 8
  28. Mohamed M. Mostafa, "More than Words : Social Networks' Text Mining for Consumer Brand Sentiments", Expert Systems with Applications, Vol. 40, Issue 10, 2013, pp. 4241-4251 https://doi.org/10.1016/j.eswa.2013.01.019
  29. Neal Schmitt, "Method Bias: The Importance of Theory and Measurement", Journal of Organizational Behavior, Vol. 15, no. 5, Wiley, 1994, pp. 393-398 https://doi.org/10.1002/job.4030150504
  30. Nikolay Archak, "Deriving the Pricing Power of Product Features by Mining Consumer Reviews", Management Science, 57-8, 2011, pp. 1485-1509 https://doi.org/10.1287/mnsc.1110.1370
  31. Nut Limsopatham and Nigel Collier, "Bidirectional LSTM for Named Entity Recognition in Twitter Messages", The COLING 2016 Organizing Committee, 2016, pp. 145-152
  32. Paul C. Tetlock, "Giving Content to Investor Sentiment: The Role of Media in the Stock Market", The Journal of Finance, Vol. 62, Issue 3, 2007, pp. 1139-1168 https://doi.org/10.1111/j.1540-6261.2007.01232.x
  33. Priyanka Vedurumudi, "Twitter Sentiment Analysis using Deep Learning", I-Manager's Journal on Computer Science, 2021, pp. 1
  34. Teresa Galanti, Gloria Guidetti, Elisabetta Mazzei, Salvatore Zappala and Ferdinando Toscano, "Work From Home During the COVID-19 Outbreak: The Impact on Employees' Remote Work Productivity, Engagement, and Stress", J Occup Environ Med ; 63(7), 2021, pp. 426-432
  35. Tim O'Reilly, "What Is Web 2.0: Design Patterns And Business Models For The Next Generation Of Software", University Library of Munich, Germany, MPRA Paper, 2007
  36. Xuefeng Wang, Xuemei Yang, Xinglu Wang, Mengge Xia and Jiayun Wang, "Evaluating the Competitiveness of Enterprise's Technology based on LDA Topic Model", Technology Analysis & Strategic Management, 32:2, 2020, pp. 208-222 https://doi.org/10.1080/09537325.2019.1648789
  37. Ya-Han Hu, Yen-Liang Chen and Hui-Ling Chou, "Opinion Mining from Online Hotel Reviews - A Text Summarization Approach", Information Processing & Management, Vol. 53, Issue 2, 2017, pp. 436-449 https://doi.org/10.1016/j.ipm.2016.12.002
  38. Yee Whye Teh, "A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes", Association for Computational Linguistics, 2006, pp. 985-992
  39. Yikai Yan and Chaoyue Niu, Yucheng Ding, Zhenzhe Zheng, Fan Wu, Guihai Chen, Shaojie Tang, Zhihua Wu, "Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availability", arXiv:2002.07399, 2020
  40. Zeng, Jianping, Duan, Jiangjiao, Wang, Wei and Wu, Chengrong, "Semantic Multi-Grain Mixture Topic Model for Text Analysis", Expert Syst. Appl. 38, 2011, pp. 3574-3579 https://doi.org/10.1016/j.eswa.2010.08.146