網際網路與電子商務的蓬勃發展促進了線上購物的交易行為，而電子商務擁有不受時間與地理因素影響的特質，讓市場範疇能夠迅速地觸及全球的消費者，對於消費者而言，透過虛擬通路來進行購物提供了更多的選擇性與便利性，並且也為企業帶來可觀的商機。越來越多人們選擇利用網路來購買商品，而為了使企業了解顧客對商品的使用體驗，多數電子商務網站皆會提供商品評價的平台服務，讓顧客分享對於商品的意見。線上顧客評論除了能夠成為企業重新設計產品的方針，也能提供潛在顧客衡量商品品質的依據，而線上顧客評論已被廣泛視為顧客購買決策中的重要元素，本研究運用線上顧客評論作為分析的資料，並透過網路爬蟲、文字探勘、分群分析、關聯規則探勘等網路資料探勘技術與知覺地圖技術，發展一個協助多項產品間差異比較的方法，藉此讓企業在眾多資料中了解顧客的心聲與競爭者商品的差異，並提供企業產品再設計與新產品定價的方向。

With the rapid development of e-commerce applications, online shopping has become more popular and convenient than in-store shopping. E-commerce provides a fast and global platform in which transactions, pre- and post-sales communications take place efficiently and rapidly. Online shopping offers a range of product options with no geographical limitations to customers. Many people choose to use the Internet to search and purchase products and, thus, Internet provides enormous opportunities to consumer goods marketers and manufacturers. In recent years, many e-commerce websites provide consumer feedback functions and social networks, allowing customers to share their purchasing and usage experiences online. Companies collect and analyze information from customers’ reviews through the platform to understand the impressions of customers for products they purchased. Online customer reviews has been widely regarded as an important source of information influencing customers buying decisions. In addition, online customer reviews help companies to redesign their products with key features that better positions to target customers in promising market sectors. This research uses online customer reviews as the business intelligence (BI) corpus. After determining the source webpage of customer reviews, a web crawler needs to collect customer review text. Afterwards, computer-assisted text mining, clustering analysis, association rule mining, and perceptual mapping are applied to develop a formal methodology to compare similar products in a given domain. In this research, the consumer electronic sector is studied. Mobile phone customer reviews are web crawled, collected, mined, and analyzed. The study assists mobile phone manufacturers to understand the voice of customers in both positive and negative perspectives of post-purchasing experiences. The customer-preferred product functions, hardware/software/app features, and price positions, as key business intelligence, are derived for new product designs and market launches.

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