Extracting Actionable Requirements from Crisis Event Tweets for Requirements Engineers

Shrestha, Tejash

Extracting Actionable Requirements from Crisis Event Tweets for Requirements Engineers

dc.contributor.advisor	Rokne, Jon George
dc.contributor.advisor	Nayebi, Maleknaz
dc.contributor.author	Shrestha, Tejash
dc.contributor.committeemember	Ovens, Katie Lyn
dc.contributor.committeemember	Barcomb, Ann
dc.date	2025-06-09
dc.date.accessioned	2025-01-02T21:32:55Z
dc.date.available	2025-01-02T21:32:55Z
dc.date.issued	2025-01-02
dc.description.abstract	Developing crisis-related software applications is a key part of crisis management, but gathering comprehensive requirements for the application is a challenge for requirements engineers, as incomplete requirements can lead to applications lacking essential features. With the rise of social media, vast amounts of public data are now available for extracting crisis-related data to be used for feature specification. However, extracting key information poses challenges. This study therefore introduces a framework for extracting actionable requirements from social media, specifically analyzing crisis related tweets using fine-tuned machine learning models. The framework identifies highly relevant tweets based on a taxonomy of sources of information and the priority of tweets, then employs tools and techniques like word clouds, emotion analysis, and topic modeling to reveal insights. We used a 2-2-step prompting approach with GPT-4o to extract requirements through actionable tweets. In our study, 87 requirements were extracted from the actionable information sources, reflecting the needs expressed during crises. These requirements were then translated into potential features for a "Crisis or Emergency Management" application. When compared with 32 existing mobile apps, it shows a 75.6% match and identifies 34 unique features for future development.
dc.identifier.citation	Shrestha, T. (2025). Extracting actionable requirements from crisis event tweets for requirements engineers (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/120337
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Requirements Extarction
dc.subject	Requirements Engineering
dc.subject	Natural Language Processing
dc.subject	Actionable Tweets
dc.subject	Large Language Model
dc.subject	Crisis Tweets
dc.subject	Machine Learning
dc.subject.classification	Computer Science
dc.title	Extracting Actionable Requirements from Crisis Event Tweets for Requirements Engineers
dc.type	master thesis
thesis.degree.discipline	Computer Science
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.