DISSERTATION DEFENSE

Author : Ningqiao Li
Advisor: Dr. Yan Tong
Date: March 24th, 2026
Time: 3:30 pm
Place: Virtual
Link:  https://teams.microsoft.com/meet/27045487833541?p=PtaQgpwzZvxJkYDZYu
Meeting ID: 270 454 878 335 41
Passcode: TP2Wm3dw
Abstract

As images have become a critical strategy for hospitality businesses to position and differentiate their brands online, and brand personality projected by brands and perceived by prospective consumers has been recognized as a key factor influencing booking decisions, this thesis investigates an understudied area: how brand personality perceived from hotel images can be automatically assessed using advanced computational models.

Specifically, this study systematically compares four model architectures (i.e., YOLO26 Nano, ResNet50, Swin-Small Transformer, and CLIP) across multiple label sources, including human rater annotations, labels generated by a single large language model (LLM) (GPT-4o), and average labels generated by multiple LLMs (GPT-4o, Gemini 2.5 Flash, and Claude Sonnet). A dataset of 2,182 hotel-generated images posted on a social media platform was annotated and evaluated across six brand personality dimensions: relaxing, hospitable, lively, distinctive, sophisticated, and wholesome.
The results demonstrate that CLIP, trained on multi-LLM averaged labels, achieves the highest performance, outperforming all image-only architectures as well as models trained on human annotations or a single LLM. This study contributes to a better understanding of how affective semantics can be effectively recognized by comparing different deep learning models and examining performance differences between models trained on human-labeled data and those trained on generative AI–labeled data. It further extends the discussion on the effectiveness of LLM-generated labels in contexts that require domain knowledge and higher-level semantic interpretation.