分析旅遊評論中之極性不一致性問題
Abstract
近年來，隨著網際網路的發展，消費者能夠在消費之後，在網路平臺上面發表自己對於此次消費的滿意程度，並留下評分供有需求的使用者參考。
本研究目的在於觀察旅店的評論內容和顧客傾向中的不一致性，評論文本內容普遍存在兩個典型的特徵，星星和評論文本內容。評論的文本內容提供了文字用以解釋給分說明，當星星和評論內容對稱(即星等和內容一致)時，會在消費者閱讀購物經驗中加深印象，且提升價值；反之，當評論內容的不確定性提高的時候，使得消費者失望和苦惱，對於消費者和企業，線上評論系統的價值也降低了。
本研究以lexicon-based的方法，不用透過人工標注的方式得到評論的極性，檢查評論當中所存在的不一致性。目的是要過濾評論文本內容傾向和使用者評分傾向不一致的評論，以提高評論資料的可信度。
Since the rapid development of internet and technology, nowadays people are used to check online reviews before they purchase products. On the other head, people can write comments or reviews to express their opinions by any device which is able to access the Internet. The purpose of our research is to observe and analyze the nonaligned travel reviews. There are two specific features in each review including the ratings and the context of reviews. The context of the review contains the description which describes the reasons for the ratings. When the rating of the review is aligned with the content of the review, the review is more persuasive and impressed. However, if the polarity of the content is not aligned with the ratings, the uncertainty grows and it makes readers confused. The results will lead to mutual disadvantage for both customers and producers and leave bad impression on the online review platform. Our research employs the lexicon-based method to avoid from annotating by manual work and finds out the reviews that are not aligned. The aim is to filter out the nonalignment reviews so as to enhance the credibility of the review system.
Since the rapid development of internet and technology, nowadays people are used to check online reviews before they purchase products. On the other head, people can write comments or reviews to express their opinions by any device which is able to access the Internet. The purpose of our research is to observe and analyze the nonaligned travel reviews. There are two specific features in each review including the ratings and the context of reviews. The context of the review contains the description which describes the reasons for the ratings. When the rating of the review is aligned with the content of the review, the review is more persuasive and impressed. However, if the polarity of the content is not aligned with the ratings, the uncertainty grows and it makes readers confused. The results will lead to mutual disadvantage for both customers and producers and leave bad impression on the online review platform. Our research employs the lexicon-based method to avoid from annotating by manual work and finds out the reviews that are not aligned. The aim is to filter out the nonalignment reviews so as to enhance the credibility of the review system.
Description
Keywords
意見探勘, 文字探勘, 旅遊評論, K-means 演算法, Opinion Mining, Text Mining, Travel Review, K-means Algorithm