近年來，Google地圖已成為目前成長最快且使用量最大的評論平台，其中，餐廳評論是Google地圖使用者最常查看的評論類別，消費者通常會透過網路評論了解其他消費者對餐廳的評價，以作為選擇餐廳的重要參考依據。然而，隨著評論數量的增加，消費者往往無法有效地過濾出有用的資訊。因此，利用資訊技術幫助人們快速從大量文字中擷取出重點，找到適合的選擇，成為一項熱門的研究。本論文透過文字探勘、機器學習、自然語言處理的技術，分析Google地圖上的餐廳評論數據，結合基於內容的過濾推薦系統，打造評論視覺化互動面板，提供使用者更加直觀地了解餐廳的評價和特點，並幫助使用者快速從大量評論擷取出重點並從中找到適合的餐廳選擇。

Google Maps has become the fastest-growing and most widely used review platform in recent years. Restaurant reviews are the most frequently viewed by Google Maps users. Consumers learn about other consumers’ opinions through online reviews, which is an important evaluation standard for choosing a restaurant. However, with the increase in the number of reviews, consumers are unable to effectively extract useful information. Therefore, how to quickly extract key points from a large amount of text and find suitable choices has become a hot topic. This study analyzes restaurant review data on Google Maps through text mining, machine learning, and natural language processing technologies, combines it with a content-based filtering recommendation system, and creates a review-based visualization dashboard. This dashboard provides users with a more intuitive understanding of restaurant reviews and characteristics, helps users quickly extract key points from many reviews, and finds suitable choices.

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