{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/956"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/956","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"Recommendations in visual analytics using emotions : a mixed-initiative interaction approach","abstract":"The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics.","abstract_html":"The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics.","abstract_has_math":false,"creators":["Panwar, Prateek"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Science (MSc)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Collins, Christopher"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-07-01","date_published":"2018-07-01","updated_at":"2026-07-24T05:35:20Z","subjects":["Affective computing","Information visualization","Recommendation system","Eye tracking","GSR"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/956","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Collins, Christopher"]},{"key":"dc:creator","label":"Author","values":["Panwar, Prateek"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-09-12T13:08:12Z","2022-03-29T17:25:48Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-09-12T13:08:12Z","2022-03-29T17:25:48Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-07-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MSc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Affective computing","Information visualization","Recommendation system","Eye tracking","GSR"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/956"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics."]},{"key":"dc:title","label":"Title","values":["Recommendations in visual analytics using emotions : a mixed-initiative interaction approach"]}]}],"canonical_facts":{"dc:contributor.advisor":["Collins, Christopher"],"dc:creator":["Panwar, Prateek"],"dc:date.accessioned":["2018-09-12T13:08:12Z","2022-03-29T17:25:48Z"],"dc:date.available":["2018-09-12T13:08:12Z","2022-03-29T17:25:48Z"],"dc:date.issued":["2018-07-01"],"dc:description.abstract":["The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics."],"dc:identifier.uri":["https://hdl.handle.net/10155/956"],"dc:language.iso":["en"],"dc:subject":["Affective computing","Information visualization","Recommendation system","Eye tracking","GSR"],"dc:title":["Recommendations in visual analytics using emotions : a mixed-initiative interaction approach"],"dc:type":["Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Master of Science (MSc)"],"thesis:institution_name":["University of Ontario Institute of Technology"]},"updated_at":"2026-07-24T05:35:20Z"}