本研究旨在基於旅遊社群大數據的情感分析，開發一種目標市場決策方法。首先，本研究採用定性情感分析方法，利用Python網頁爬蟲技術收集酒店預訂網站Booking.com和旅遊行程網站Klook的顧客評論，並計算評論中每個詞彙的平均分數和重要性，以進行定性情感分析。接著，根據情感分析結果，本研究進一步應用量化決策樹方法，設計問卷並收集有效回應共305份，隨後利用SPSS Modeler中的決策樹方法對目標市場進行細分。根據情感分析與決策樹分析結果，本研究為旅遊服務平台與供應商提供針對不同目標市場細分的行銷建議。本研究是基於旅遊社群大數據的情感分析來制定目標市場決策的先導性研究。

This study aims to develop a method for making target market decisions based on sentiment analyses of travel communities’big data. This study first adopts a qualitative sentiment analysis method involving the use of Python Web Crawler technology to collect customer comments on a hotel booking website, Booking.com, and a travel itinerary website, Klook. The mean score and importance of each word in the comments are calculated for the qualitative sentiment analysis. Later, based on the sentiment analysis results, this study applies the quantitative decision tree method, and a questionnaire is designed. The 305 valid responses obtained are analyzed using the decision tree method in SPSS Modeler to segment the target market. Based on the sentiment analysis and the decision tree analysis results, this study provides marketing suggestions to travel services platforms and providers regarding the various target market segments. This study is a pioneering effort to develop a method for making target market decisions based on sentiment analyses of travel communities’big data.