自從網路邁入Web2.0 的時代之後，網路平台的蓬勃發展日新月異，資訊設備也逐漸普及化，家家戶戶從桌上型電腦、筆記型電腦、平板電腦乃至於人手一支的行動智慧型手機，使用者在網路上留下的產品使用建議以及意見與評價數量龐大，這些訊息不乏其有建設性的創意構想，如何過濾並使得這些有效建議成為公司開發產品的依據是一個重要課題。
網路上充斥著大量可瀏覽參考的文字資料，如何找出可信任的資源是一個很重要的問題。在各色評論當中首先關注的就是產品的星等、評論的有用性等可以快速讓自己獲取需求資訊的重要標籤，本研究欲探討這些在評論中得到的資訊之間的關係，擷取網路上的使用者生成資料(user-generated content)並結合文字探勘的LDA(Latent Dirichlet Allocation)技術分析亞馬遜(Amazon)電子商務平台上的客戶評論資料，並得到數據化的資料後再結合階層線性模型(Hierarchical linear model)，從資料之間的層次構面去進行相關性分析，找出更貼近消費者需求的主題，從而使得企業能夠以更主動了解既有客群的潛在需求。

Since the era of Web 2.0, the rapid development of the Internet platform has become increasingly popular, and information devices have become popular. Every household has a mobile phone from desktop computers, notebook computers, tablets, and even human hands. The user's recommendation on the use of the product on the Internet and the large number of opinions and evaluations are numerous. These messages are not lacking in constructive creative ideas. How to filter and make these effective suggestions become the basis for the company to develop products is an important issue.
The Internet is full of texts that can be viewed and referenced. How to find trusted resources is an important issue. The first concern in the various comments is the star of the product, the helpfulness of the comment, etc., which can quickly get the important information of the demand information. The research wants to explore the relationship between the information obtained in the comments, and draw on the network. User-generated content and LDA (Latent Dirichlet Allocation) technology for text analysis to analyze customer reviews on Amazon's e-commerce platform, and to obtain data and then combine HLM (hierarchical linear models), from the hierarchical structure of the data to conduct correlation analysis to find a topic closer to the needs of consumers, so that companies can more actively understand the potential needs
of existing customers.

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