Text Mining to Understand Emotion Triggers
dc.contributor.author | Chen, Liuyan | |
dc.date.accessioned | 2019-05-15T13:45:59Z | |
dc.date.available | 2020-05-15T04:50:07Z | |
dc.date.issued | 2019-05-15 | |
dc.date.submitted | 2019-04-30 | |
dc.description.abstract | In computational linguistics, most sentiment analysis builds binary classification models on customer reviews data to predict whether a review is positive or negative. In this thesis, we go a step further and build interpretable classification models to predict fine-grained emotions associated with text (such as happy, sad, productive and tired). This analysis is enabled by a unique journaling dataset containing short pieces of text and associated emotional status self-reported by writers. To further study what people feel emotional about (emotion triggers), we perform model interpretation. We make two main contributions. First, we apply state-of-the-art text mining methodologies to extract emotion triggers from text, during which we discover and solve an issue with the attention mechanism in a popular deep learning model (Dynamic Memory Network). Second, we obtain data-driven evidence of emotion triggers based on a group of 67,000 people, which contributes to a better understanding of emotion triggers from the perspective of public mental health. | en |
dc.identifier.uri | http://hdl.handle.net/10012/14636 | |
dc.language.iso | en | en |
dc.pending | false | |
dc.publisher | University of Waterloo | en |
dc.title | Text Mining to Understand Emotion Triggers | en |
dc.type | Master Thesis | en |
uws-etd.degree | Master of Applied Science | en |
uws-etd.degree.department | Management Sciences | en |
uws-etd.degree.discipline | Management Sciences | en |
uws-etd.degree.grantor | University of Waterloo | en |
uws-etd.embargo.terms | 1 year | en |
uws.contributor.advisor | Golab, Lukasz | |
uws.contributor.affiliation1 | Faculty of Engineering | en |
uws.peerReviewStatus | Unreviewed | en |
uws.published.city | Waterloo | en |
uws.published.country | Canada | en |
uws.published.province | Ontario | en |
uws.scholarLevel | Graduate | en |
uws.typeOfResource | Text | en |