美髮服務屬於高度互動且情境依賴的產業，顧客的體驗與評論對品牌營運與服務設計具有深遠影響。過往業者多依賴問卷或現場反饋了解顧客需求，然而這類方式不僅耗費人力與時間，也難以即時掌握顧客語意傾向。隨著雲端科技與人工智慧（Artificial Intelligence, AI）技術的進展，業者已可透過數據驅動工具進行非結構化語料的自動分析與推薦生成。特別是生成式人工智慧（Generative AI, GenAI）與文字探勘技術的結合，為顧客互動與行銷溝通帶來嶄新可能。
本研究以A科技公司雲端管理系統所蒐集之3,000筆美髮顧客評論與消費紀錄為語料基礎，透過中文斷詞（CKIP、jieba）、TF-IDF特徵抽取、LDA主題建模與SnowNLP情緒分析，建立顧客語意特徵向量，再分別以LSTM、Transformer、BERT分類器與GPT-2進行模型訓練與生成語句分析，驗證其在個人化推薦與語句生成上的應用可行性。結果顯示，GPT-2微調模型於語境理解與推薦自然度表現最佳（F1-score達0.86），亦獲得設計師實務回饋肯定，表示有助於縮短溝通流程、強化顧客信任，尤其對新手設計師具備輔助價值。
綜合而言，文字探勘模組可有效萃取顧客評論中潛藏的偏好語意與情緒傾向，成為AI推薦系統中理解層的關鍵技術，搭配生成式模型則可即時產出具情境適應性的推薦語句，能支援第一線設計師進行個人化服務溝通。本研究建構之AI整合架構，具備技術成熟度與商業應用潛力，未來可拓展至美容、健身與醫療等高互動服務產業，成為傳統產業智慧轉型的可行方案。

Hairdressing services are characterized by high interactivity and context dependency, where customer experiences and reviews exert significant influence on service design. Traditionally, hairdressing businesses relied on surveys or on-site feedback to understand customer needs. However, such methods are time-consuming, labor-intensive, and often fail to capture customers’ semantic tendencies. With the advancement of cloud computing and artificial intelligence (AI), service providers could leverage data-driven tools to conduct automated analysis and generate recommendations from unstructured textual data. Specifically, the integration of generative AI (GenAI) and text mining techniques for better customer engagement and marketing communication is the main target of this research.
This study develops a general process through Chinese word segmentation (CKIP and jieba), TF-IDF feature extraction, LDA topic modeling, and SnowNLP sentiment analysis, and customer semantic feature vectors could be constructed. These vectors were then used to train and to evaluate sentence generation across four models: LSTM, Transformer, BERT classifier, and GPT-2. Based on 3,000 customer reviews and transaction records collected from the cloud-based management system of a case company, the results demonstrated that the fine-tuned GPT-2 model achieved the best performance in contextual comprehension and naturalness of recommendation (with an F1-score of 0.81), and received positive feedback from professional hair stylists. The findings indicate that text mining modules effectively extract implicit preferences and emotional tendencies embedded in customer reviews. The proposed AI architecture developed in this study demonstrates both technological feasibility and its commercial potential.

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