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      KCI등재 SCOPUS

      Emotion-based analysis of programming languages on Stack Overflow

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      https://www.riss.kr/link?id=A107280988

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      다국어 초록 (Multilingual Abstract)

      When developing a software engineering project, selecting the most appropriate programming language is a crucial step. Most often, feeling at ease with the possible options becomes almost as relevant as the technical features of the language. Therefor...

      When developing a software engineering project, selecting the most appropriate programming language is a crucial step. Most often, feeling at ease with the possible options becomes almost as relevant as the technical features of the language. Therefore, it appears to be worth analyzing the role that the emotional component plays in this process.
      In this article, we analyze the trend of the emotions expressed by developers in 2018 on the Stack Overflow platform in posts concerning 26 programming languages. To do so, we propose a learning model trained by distant supervision and the comparison of two different classifier architectures.

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      참고문헌 (Reference)

      1 A. Go, "Twitter sentiment classification using distant supervision" 2009-, 2009

      2 E. Guzman, "Towards emotional awareness in software development teams" 671-674, 2013

      3 N. Novielli, "The challenges of sentiment detection in the social programmer ecosystem" 33-40, 2015

      4 P. Fornacciari, "Social network and sentiment analysis on Twitter: Towards a combined approach" 53-64, 2015

      5 F. Calefato, "Sentiment polarity detection for software development" 23 (23): 1352-1382, 2018

      6 B. Liu, "Sentiment analysis and opinion mining" 5 (5): 1-167, 2012

      7 M.R. Islam, "Leveraging automated sentiment analysis in software engineering" 203-214, 2017

      8 U. Fayyad, "From data mining to knowledge discovery in databases" 17 (17): 37-, 1996

      9 G. Angiani, "Flat and hierarchical classifiers for detecting emotion in tweets" Springer 51-64, 2016

      10 W.G. Parrott, "Emotions in Social Psychology: Essential Readings" Psychology Press 2001

      1 A. Go, "Twitter sentiment classification using distant supervision" 2009-, 2009

      2 E. Guzman, "Towards emotional awareness in software development teams" 671-674, 2013

      3 N. Novielli, "The challenges of sentiment detection in the social programmer ecosystem" 33-40, 2015

      4 P. Fornacciari, "Social network and sentiment analysis on Twitter: Towards a combined approach" 53-64, 2015

      5 F. Calefato, "Sentiment polarity detection for software development" 23 (23): 1352-1382, 2018

      6 B. Liu, "Sentiment analysis and opinion mining" 5 (5): 1-167, 2012

      7 M.R. Islam, "Leveraging automated sentiment analysis in software engineering" 203-214, 2017

      8 U. Fayyad, "From data mining to knowledge discovery in databases" 17 (17): 37-, 1996

      9 G. Angiani, "Flat and hierarchical classifiers for detecting emotion in tweets" Springer 51-64, 2016

      10 W.G. Parrott, "Emotions in Social Psychology: Essential Readings" Psychology Press 2001

      11 L.A. Cabrera-Diego, "Classifying emotions in stack overflow and jira using a multi-label approach" 195 :

      12 S. Cagnoni, "Automatic creation of a large and polished training set for sentiment analysis on Twitter" Springer 146-157, 2017

      13 G. Lombardo, "A combined approach for the analysis of support groups on Facebook-The case of patients of Hidradenitis Suppurativa" 78 (78): 3321-3339, 2019

      14 P. Fornacciari, "A case-study for sentiment analysis on Twitter" 53-58, 2015

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2023 평가예정 해외DB학술지평가 신청대상 (해외등재 학술지 평가)
      2020-01-01 평가 등재학술지 유지 (해외등재 학술지 평가) KCI등재
      2017-08-01 평가 SCOPUS 등재 (기타) KCI등재
      2017-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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