{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:49:17Z","timestamp":1769636957308,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72271053"],"award-info":[{"award-number":["72271053"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The acquisition of physiological signals for analyzing emotional experiences has been intrusive, and potentially yields inaccurate results. This study employed infrared thermal images (IRTIs), a noninvasive technique, to classify user emotional experiences while interacting with business-to-consumer (B2C) websites. By manipulating the usability and aesthetics of B2C websites, the facial thermal images of 24 participants were captured as they engaged with the different websites. Machine learning techniques were leveraged to classify their emotional experiences, with participants\u2019 self-assessments serving as the ground truth. The findings revealed significant fluctuations in emotional valence, while the participants\u2019 arousal levels remained consistent, enabling the categorization of emotional experiences into positive and negative states. The support vector machine (SVM) model performed well in distinguishing between baseline and emotional experiences. Furthermore, this study identified key regions of interest (ROIs) and effective classification features in machine learning. These findings not only established a significant connection between user emotional experiences and IRTIs but also broadened the research perspective on the utility of IRTIs in the field of emotion analysis.<\/jats:p>","DOI":"10.3390\/s23187991","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T22:38:45Z","timestamp":1695249525000},"page":"7991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification of User Emotional Experiences on B2C Websites Utilizing Infrared Thermal Imaging"],"prefix":"10.3390","volume":"23","author":[{"given":"Lanxin","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China"}]},{"given":"Wenzhe","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China"}]},{"given":"Han","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China"}]},{"given":"Chengqi","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southeast University, 2 Southeast University Road, Nanjing 211189, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, R., and Sun, T. 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