消費者線上評論是消費者針對產品或服務所表達之意見或建議，這些評論是公司的重要資產，它們不僅能幫助公司設計與調整其經營策略以建立良好的聲譽及消費者忠誠度，也能夠幫助消費者更有效率地做出購買決策。為了提昇線上市場交易互動的品質，電子商務平台亦需要提供出色的服務以保持其營運績效。因此，本研究旨在運用文字探勘中之主題模型（topic modeling）及情感分析（sentiment analysis），透過分析消費者之線上評論以評估電子商務之營運績效。本研究採用潛在狄利克雷分配（latent Dirichlet allocation, LDA）主題模型來辨別消費者評論的主題，接著透過意見極性分類以確認哪些主題被界定為正面或負面之情緒。基於消費者線上評論之資料集，將能評估物流層面以及管理因素，包括到貨時間、退款或退貨策略以及整體顧客管理互動等之優劣，因此本研究之結果將可從顧客評論中找出顧客關切之重要面向，並據以評估及瞭解電子商務平台在顧客心目中的表現，以提昇平台之服務及物流之績效.

Online reviews provide opportunities for consumers to voice their opinions, evaluations, and recommendation regarding the products or services. These valuable resources can help a company in designing its business strategy, specifically in building a good reputation and consumer loyalty. On the other hand, the reviews also can help the consumer in purchase decision making. Electronic commerce (e-commerce) platform, which provides an online marketplace, also needs to maintain its performance to deliver excellent services. Therefore, this study aims to analyze and exploit information regarding the performance of e-commerce by using topic modeling and sentiment analysis. In particular, latent Dirichlet allocation (LDA) topic modeling is used to identify the dominant topics in consumer reviews. Then, these topics are evaluated by sentiment polarity classification to determine which topic gives a positive or negative sentiment. Based on the online consumer review data set, the evaluations include the logistics aspect and managerial factors such as delivery time, refunds or returns policy, and management accessibility. The results of this study will provide the aspect classification to evaluate and understand the performance of e-commerce, especially the services and logistics performance.

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