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研究生: 林彤
Lin, Tung
論文名稱: 分析旅遊評論中之極性不一致性問題
Analyzing Polarity Nonalignment Problem from Travel Reviews
指導教授: 侯文娟
Hou, Wen-Juan
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2019
畢業學年度: 107
語文別: 中文
論文頁數: 51
中文關鍵詞: 意見探勘文字探勘旅遊評論K-means 演算法
英文關鍵詞: Opinion Mining, Text Mining, Travel Review, K-means Algorithm
DOI URL: http://doi.org/10.6345/THE.NTNU.DCSIE.002.2019.B02
論文種類: 學術論文
相關次數: 點閱:180下載:0
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  • 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.

    第一章 緒論 1 第一節 研究動機 1 第二節 論文架構 2 第二章 相關研究探討 3 第一節 自然語言處理 3 第二節 中文斷詞系統 4 第三節 情緒語義分析 7 第四節 意見探勘 10 第五節 臺灣大學意見詞詞典(NTUSD) 11 第六節 中文維度型字典(CVAW) 13 第三章 研究方法 15 第一節 架構圖 15 第二節 資料集來源及處理方式 18 第三節 K-means 演算法 33 第四章 實驗結果與分析 36 第一節 情緒詞分布情形分析 36 第二節 擴充情緒詞典 41 第三節 K-means 分群結果討論 43 第五章 結論與未來展望 47 第一節 結論 47 第二節 未來展望 48 參考著作 49

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