Composite Machine Learning System for Real-Time Response to Negative Online Reviews: A Case Study Based on the Negative Reinforcement Model of Digital Marketing †
學年 114
學期 1
出版（發表）日期 2025-12-25
作品名稱 Composite Machine Learning System for Real-Time Response to Negative Online Reviews: A Case Study Based on the Negative Reinforcement Model of Digital Marketing †
作品名稱（其他語言）
著者 Chien-Hung Lai; Yaonan Hung; Yi Lin; Tzu-Shuang Liu
單位
出版者
著錄名稱、卷期、頁數 Engineering Proceedings 120(1), p.5
摘要 This research proposes a composite machine learning (ML) framework for real-time response to negative online reviews, grounded in the psychological principle of negative reinforcement. By integrating K-means clustering to group reviews by thematic similarity and bidirectional encoder representations from transformer (BERT)-based sentiment analysis to assess emotional tone, and the system identifies high-risk clusters requiring marketing intervention. Customized response strategies are designed based on cluster sentiment intensity, and their effectiveness can be evaluated via sentiment transformation functions. The proposed model provides a practical and adaptive approach to digital marketing, enabling brands to respond rapidly, reduce dissatisfaction, and enhance consumer trust in a data-driven environment.
關鍵字 negative reinforcement; psychological; sentiment analysis; BERT; K-means
語言 en
ISSN 2673-4591
期刊性質 國外
收錄於
產學合作
通訊作者 Yaonan Hung
審稿制度 是
國別 CHE
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128342 )