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Affective Sentiment and Emotional Analysis of Pull Request Comments on GitHub

dc.contributor.authorRishi, Deepak
dc.date.accessioned2017-12-15T15:15:16Z
dc.date.available2017-12-15T15:15:16Z
dc.date.issued2017-12-15
dc.date.submitted2017-12-11
dc.description.abstractSentiment and emotional analysis on online collaborative software development forums can be very useful to gain important insights into the behaviors and personalities of the developers. Such information can later on be used to increase productivity of developers by making recommendations on how to behave best in order to get a task accomplished. However, due to the highly technical nature of the data present in online collaborative software development forums, mining sentiments and emotions becomes a very challenging task. In this work we present a new approach for mining sentiments and emotions from software development datasets using Interaction Process Analysis(IPA) labels and machine learning. We also apply distance metric learning as a preprocessing step before training a feed forward neural network and report the precision, recall, F1 and accuracy.en
dc.identifier.urihttp://hdl.handle.net/10012/12728
dc.language.isoenen
dc.pendingfalse
dc.publisherUniversity of Waterlooen
dc.subjectSentiment analysisen
dc.subjectEmotional Analysisen
dc.subjectDeep Learningen
dc.subjectMachine Learningen
dc.subjectNatural Language Processingen
dc.subjectDistance Metric Learningen
dc.titleAffective Sentiment and Emotional Analysis of Pull Request Comments on GitHuben
dc.typeMaster Thesisen
uws-etd.degreeMaster of Mathematicsen
uws-etd.degree.departmentDavid R. Cheriton School of Computer Scienceen
uws-etd.degree.disciplineComputer Scienceen
uws-etd.degree.grantorUniversity of Waterlooen
uws.contributor.advisorHoey, Jesse
uws.contributor.affiliation1Faculty of Mathematicsen
uws.peerReviewStatusUnrevieweden
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.scholarLevelGraduateen
uws.typeOfResourceTexten

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