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Ďurček, Martin: Sentiment analysis and linking of social media with open-source software repositories. 2018
Inhalt
  • [1]Titelblatt
  • [3]Abstract
  • [6]Contents
  • 3Chapter 1 Introduction
    • 41.1 Motivation
  • 5Chapter 2 Goal and proposed framework
  • 9Chapter 3 Related work
  • 13Chapter 4 Social Media
    • 134.1 Potential of social media data mining
    • 144.2 Data
      • 144.2.1 Getting data
  • 19Chapter 5 Open Source Projects Analysis
    • 195.1 History of Open-Source Software
    • 205.2 Types of Open-Source Software analysis
    • 205.3 Choosing projects of interest
    • 215.4 GitHub mining
      • 215.4.1 Release dates
      • 235.4.2 Issues
  • 25Chapter 6 Sentiment analysis
    • 266.1 History
    • 276.2 Training datasets
    • 286.3 Language processing tools
    • 296.4 Performance metrics
    • 316.5 Classifier evaluation
    • 396.6 Data to analyze
    • 396.7 Results
      • 396.7.1 Results for weekly collected data
      • 416.7.2 Immediate sentiment change with releases
      • 426.7.3 Cross-correlation between releases and sentiment change
      • 466.7.4 Commits count within releases
  • 49Chapter 7 Linking bug repositories and social media
    • 497.1 Approaches
    • 527.2 Available data
    • 537.3 Similarity results
      • 557.3.1 Stack Overflow
      • 587.3.2 Reddit
    • 597.4 Using GIT labels
  • 61Chapter 8 Answers and Discussion
    • 618.1 Research questions
    • 618.2 Discussion
      • 618.2.1 Release frequency and sentiment (RQ1)
      • 628.2.2 Release effect on sentiment (RQ2)
      • 628.2.3 Relation between release and sentiment (RQ3)
      • 638.2.4 Links between social media and version-control or bug-tracking system (RQ4)
  • 65Chapter 9 Future work
  • [72]Appendix
  • 77Bibliography

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