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 Lai1 , Yaonan Hung2,* , Yi Lin3 and Tzu-Shuang Liu3
單位
出版者
著錄名稱、卷期、頁數 Eng. Proc. 2025, 120(1), 5; https://doi.org/10.3390/engproc2025120005
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
期刊性質 國外
收錄於
產學合作
通訊作者 洪耀南 hung Yaonan
審稿制度 1
國別 JPN
公開徵稿
出版型式 ,電子版,紙本