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primary topic: Comprehensive Analysis of Beauty Community Discourse on TikTok Through GPT Embeddings and BERTopic Modeling

Comprehensive Analysis of Beauty Community Discourse on TikTok Through GPT Embeddings and BERTopic Modeling
type: AcademicArticle
type: Article
abstract: 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 (cv, umass, cuci, cnpmi) 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). Acv 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.
date: 2025
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

Institute of Electrical and Electronics Engineers Inc.
type: Organization

K. S. Savita
type: Person
given name: K. S.
family name: Savita

Muhammad Akmal Hakim Ahmad Asmawi
type: Person
given name: Muhammad Akmal Hakim Ahmad
family name: Asmawi

Pradeep Isawasan
type: Person
given name: Pradeep
family name: Isawasan

Song Quan Ong
type: Person
given name: Song Quan
family name: Ong

Boonyaik Yaik Ooi
type: Person
given name: Boonyaik Yaik
family name: Ooi

HTML Summary of #20415 Comprehensive Analysis of Beauty Community Discourse on TikTok Through GPT Embeddings and BERTopic Modeling
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primary topic: Comprehensive Analysis of Beauty Community Discourse on TikTok Through GPT Embeddings and BERTopic Modeling