現今社會中旅遊已經成為人們生活中的一部分，出外旅行時難免需要一個住所，因此住宿服務產業也就隨之蓬勃。由於網路資訊發達，消費者在規劃旅遊行程時已不必親自前往旅行社詢問旅遊資訊，自行在家中使用個人電腦瀏覽景點及旅館的資訊即可。
許多消費者在決定要入住哪間旅館前，需要取得旅館相關資訊以協助他們做決策，其中最重要的資訊來源就是之前的消費者所撰寫的評論。但Web 2.0盛行使網路上評論數量快速成長，消費者已不易以人工閱讀方式將所有評論看完，因此設計一套自動分析評論的系統就可以協助使用者更有效率的了解評論內容。
在自動分析消費者撰寫之評論時，最主要的目的在於了解該消費者在評論中所表達之意見為正向(讚賞)或負向(批評)，利用情感分析技術即可辨別出消費者在評論中想表達之意見。在情感分析方法中，需要知道意見字詞之描述對象，才能釐清其真正含意，在本研究中意見字詞之描述對象稱為特徵，本研究提出非監督式之學習方法，藉由詞性排列模式來幫助系統自動擷取出旅館之特徵，並且加上多源辭典情感分析技術，取出評論中重要的部分(即消費者之意見)，透過摘要的方式將評論整合，提供消費者了解旅館的途徑，以利消費者能夠縮短選擇旅館之決策時間。
最終透過實驗來驗證本研究所提出之方法，而實驗結果顯示本研究提出之方法能夠有效地擷取出旅館之特徵，評估指標F-measure達到.628，在特徵擷取表現上優於過去之研究。

Nowadays, tourism has become a part of life. Before reserving hotels, customers need some information, which the most important source is online reviews, about hotels to help them make decisions. Due to the dramatic growing of online reviews, it is impossible for customers to read all reviews manually. Therefore, designing an automatic review analysis system, which summarizes reviews, is necessary. The main purpose of the system is to understand the opinion of reviews, which may be positive or negative. In other words, the system would analyze whether the customers who visited the hotel like it or not. Using sentiment analysis methods will help the system achieve the purpose. In sentiment analysis methods, the target of opinion (we call it “feature”) should be recognized to clarify the polarity of the opinion because polarity of the opinion may be ambiguous. Hence, we propose an unsupervised method using Part-Of-Speech pattern and multi-lexicons sentiment analysis to summarize all reviews and help customers know hotels as well as make decisions efficiently. Experimental results show that our method outperforms the state of the art methods with F-measure .628.

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