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Opinion Mining for Software Engineering
The document discusses the application of opinion mining and sentiment analysis in software engineering, examining its effectiveness, tools, and techniques. It raises research questions regarding the use of these methods in various software engineering activities and identifies available datasets for performance evaluation. Additionally, it highlights concerns about the reliability and adaptation of tools specific to software engineering contexts.
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Opinion Mining for Software Engineering
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- 2. Bin Lin GabrieleBavota Michele Lanza Nathan Cassee Nicole Novielli
- 4. sentiment polarity and positivitydegree identi f ication Bo Pang, Lillian Lee (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1–135 subjectivity detection and opinion identi f ication joint topic-sentiment analysis viewpoints and perspectives other non-factual information https://miro.medium.com/max/2040/1*YFroPGj9dpPx7nqf045AUQ.png https://www.elephango.com/images/RCLG/check-13622.jpg https://www.monocubed.com/wp-content/uploads/2020/11/Vue-vs-React-Comparison-of-Best-JavaScript-Frameworks.jpg xkcd
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- 8. https://upload.wikimedia.org/wikipedia/commons/5/5a/Books_HD_%288314929977%29.jpg ② abstract full paper ① ② ③ ④ ⑤ ⑥ ("opinion mining"OR "sentiment analysis" OR “emotion") AND (“software") AND ("developer" OR "development")
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- 10. RQ1 In whichsoftware engineering activities has opinion mining been applied? RQ2 What publicly available opinion mining tools have been adopted/developed to support these activities? RQ3 How often do researchers evaluate the reliability of opinion mining tools when they adopt the tools out-of-the box? RQ4 Which opinion mining techniques have been compared in terms of performance and in what contexts? RQ5 Which datasets are available for performance evaluation of opinion mining techniques in software-related contexts and how are they curated? RQ6 What are the concerns raised or the limitations encountered by researchers when using opinion mining techniques?
- 11. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207
- 12. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 When should we plan end-of-life of a system or a component? Which architecture alternative looks more promising? How to adapt the planning if setback is experienced by developers?
- 13. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 When should we plan end-of-life of a system or a component? Which architecture alternative looks more promising? How to adapt the planning if setback is experienced by developers?
- 14. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 assessing technique/API discovering rationale API usage general user satisfaction user-reported issues/requests identifying requirements detecting emotion/sentiment relating emotion/sentiment to performance evaluating trust
- 15. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 assessing technique/API discovering rationale API usage general user satisfaction user-reported issues/requests identifying requirements detecting emotion/sentiment relating emotion/sentiment to performance evaluating trust Lin et al. ICSE 2019
- 16. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 assessing technique/API discovering rationale API usage general user satisfaction user-reported issues/requests identifying requirements detecting emotion/sentiment relating emotion/sentiment to performance evaluating trust Di Sorbo et al. ASE 2015
- 17. RQ1: In whichsoftware engineering activities has opinion mining been applied? ISO/IEC/IEEE12207 assessing technique/API discovering rationale API usage general user satisfaction user-reported issues/requests identifying requirements detecting emotion/sentiment relating emotion/sentiment to performance evaluating trust
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- 23. general purpose SE-speci f ic sentimentSentiStrength NLTK Stanford CoreNLP Watson Natural Language Understanding* Microsoft Azure Text Analytics* TextBlob A ff in USent Syuzhet Pattern Rosette* Aylien* Narayanan et al., 2013 SentiStrength-SE Senti4SD SEntiMoji SentiSW SentiCR SentiSE emotion LIWC* TensiStrength NTUA - SLP Deva MarValous EmoTxT politeness politeness tool trust Trust-Framework opinion LDA TwitterLDA ARdoc Ticket-Tagger SURF MARC 3.0 RE - SWOT DeepTip POME
- 24. general purpose SE-speci f ic sentimentSentiStrength NLTK Stanford CoreNLP Watson Natural Language Understanding* Microsoft Azure Text Analytics* TextBlob A ff in USent Syuzhet Pattern Rosette* Aylien* Narayanan et al., 2013 SentiStrength-SE Senti4SD SEntiMoji SentiSW SentiCR SentiSE emotion LIWC* TensiStrength NTUA - SLP Deva MarValous EmoTxT politeness politeness tool trust Trust-Framework opinion LDA TwitterLDA ARdoc Ticket-Tagger SURF MARC 3.0 RE - SWOT DeepTip POME
- 25. general purpose SE-speci f ic sentimentSentiStrength NLTK Stanford CoreNLP Watson Natural Language Understanding* Microsoft Azure Text Analytics* TextBlob A ff in USent Syuzhet Pattern Rosette* Aylien* Narayanan et al., 2013 SentiStrength-SE Senti4SD SEntiMoji SentiSW SentiCR SentiSE emotion LIWC* TensiStrength NTUA - SLP Deva MarValous EmoTxT politeness politeness tool trust Trust-Framework opinion LDA TwitterLDA ARdoc Ticket-Tagger SURF MARC 3.0 RE - SWOT DeepTip POME Jongeling et al.: General-purpose sentiment analysis tools are not reliable when applied to software engineering texts (EMSE 2017) Many SE-speci f ic tools are not reused Many SE-speci f ic tools are not reused Many SE-speci f ic tools are not used
- 26. general purpose SE-speci f ic sentimentSentiStrength NLTK Stanford CoreNLP Watson Natural Language Understanding* Microsoft Azure Text Analytics* TextBlob A ff in USent Syuzhet Pattern Rosette* Aylien* Narayanan et al., 2013 SentiStrength-SE Senti4SD SEntiMoji SentiSW SentiCR SentiSE emotion LIWC* TensiStrength NTUA - SLP Deva MarValous EmoTxT politeness politeness tool trust Trust-Framework opinion LDA TwitterLDA ARdoc Ticket-Tagger SURF MARC 3.0 RE - SWOT DeepTip POME Novielli et al.: Different SE-applications require tool adaptation (MSR 2020)
- 27. 2012 2013 20142015 2016 2017 2018 2019 SentiStrength politeness LDA NLTK LIWC* Senti4SD Stanford Core NLP SentiStrength-SE Watson Natural Language* Rosette* TwitterLDA SentiSE SentiCR Pattern* Aylien* Syuzhet EmoTxT Reliability of the tools is rarely evaluated threatening conclusions of the studies.
- 28. 2012 2013 20142015 2016 2017 2018 2019 SentiStrength politeness LDA NLTK LIWC Senti4SD Stanford Core NLP SentiStrength-SE Watson Natural Language Rosette TwitterLDA SentiSE SentiCR Pattern Aylien Syuzhet EmoTxT Reliability of the tools is rarely evaluated threatening conclusions of the studies.
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- 32. 341 - 4000sentences 500 - 4800 texts sentiment and emotion content 712 - 12000 sentences 100 - 7100 texts
- 33. 341 - 4000sentences 500 - 4800 texts sentiment and emotion content 712 - 12000 sentences 100 - 7100 texts Pre-train model Fine-tune model Repurpose model General purpose data SE data Model 1 Model 2 Classi f ier Robbes and Janes. ICSE NIER 2019
- 34. https://cdn.pixabay.com/photo/2013/02/23/20/33/seagull-85512_1280.jpg Imtiaz et al.SEmotion 2018: Even human ratings had low sentiment and politeness consistency on GitHub comments.
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