TY  - CONF
A1  - Isawasan, Pradeep
A1  - Asmawi, Muhammad Akmal Hakim Ahmad
A1  - Ong, Song Quan
A1  - Ooi, Boonyaik Yaik
A1  - Savita, K. S.
PB  - Institute of Electrical and Electronics Engineers Inc.
N2  - Short-form comments on TikTok's Beauty & Personal Care videos are rich yet noisy signals of consumer sentiment. This study deploys a transformer-based pipeline that couples GPT-derived sentence embeddings with BERTopic to surface salient discussion themes. We scraped 3,912 videos from the 20 highest-revenue beauty influencers and retrieved 34,597 engagement-ranked comments posted between January and August 2024. After a GPT -powered normalisation step that expands slang, translates Malay abbreviations, and converts emoji to text, comments were embedded with OpenAI's text-embedding-3-small model, reduced via UMAP, clustered using HDBSCAN, and distilled into topics with BERTopic. Topic quality was evaluated with four coherence metrics (c<inf>v</inf>, u<inf>mass</inf>, c<inf>uci</inf>, c<inf>npmi</inf>) to ensure semantic consistency. The model revealed two coherent, non-overlapping themes: product-usage experiences (e.g., frequency of use, perceived results) and product-feature commentary (e.g., scent, packaging, variants). Ac<inf>v</inf> score of 0.620 indicates strong interpretability despite the brevity and informality of TikTok discourse. These findings show that embedding-based topic modeling can unearth actionable insights: influencers should foreground authentic usage outcomes, while brands can boost engagement by highlighting sensory cues in visual storytelling. The study demonstrates the viability of LLM -enhanced analytics for short social media text and provides a replicable framework for future TikTok commerce research, noting limitations and opportunities for multi-category, cross-regional, and multimodal extensions. © 2025 IEEE.
SP  - 444 
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105023663181&doi=10.1109%2FAiDAS67696.2025.11213658&partnerID=40&md5=29a5999867ca6a8f374e658707f42355
SN  - 9798331586034
Y1  - 2025///
ID  - scholars20415
N1  - Cited by: 0
EP  -  448
AV  - none
KW  - Embeddings; Beauty care; Beauty discourse; Bertopic; Comprehensive analysis; GPT; Noisy signals; Personal care; Tiktok; Topic Modeling; Semantics
TI  - Comprehensive Analysis of Beauty Community Discourse on TikTok Through GPT Embeddings and BERTopic Modeling
ER  -