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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉鋼(Kang Liu) | |
| dc.contributor.author | Hau Sin Chan | en |
| dc.contributor.author | 陳浩欣 | zh_TW |
| dc.date.accessioned | 2021-06-16T16:02:09Z | - |
| dc.date.available | 2023-02-01 | |
| dc.date.copyright | 2021-02-20 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-02-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62405 | - |
| dc.description.abstract | 隨網路時代的普及，旅行者在安排旅行住宿時多以網路線上預訂，而這樣的的預訂方式加上飯店住宿的體驗性商品特性，讓旅行者在預定前會非常在意過去的線上評等、評論，作為參考及決策的依據，故線上評等及評論對於消費者及業者來說都是重要指標。在進行評等及評論時，消費者會根據不同的面向去評斷該次住宿的體驗，如 Tripadvisor 就提供消費者 6 種不同的面向去進行評價，而文字評論也讓消費者能更詳細描述整個住宿體驗之感受。因此本研究透過 Tripadvisor 網站中臺北地區的飯店資料，透過文本分析將評論進行主題特徵分類及情感分析，探討評等及評論，研究消費者評論與面向評等之間的關係，並從評論中找出新的潛在面向。此外，探討展望理論中所提到在得到同等程度的獲利與損失時，會產生損失影響較大的非理性思考，本研究將應用正、負情感分數對評等的影響來驗證。 研究結果顯示評論是能夠解釋消費者給出的面向評等，而也從評論中找出餐廳飲食及環境設施這兩種面向，說明了對於業者及潛在消費者來說在缺少面向評等資訊時，透過消費者評論也可了解其面向感受。業者在想提升住宿體驗時除了原本的 6 種面向，也可在飲食及環境上檢討改進。此外也驗證了在增加相同程度的正、負面情緒時，負面情緒所帶來的影響更大，從消費者評等上來看，負面情緒的增加比起正面情緒會讓消費者在給出評等上扣更多的分數，因此業者若要提高自家的評等，對於負面評論的重視及處理要比正面評論更加積極。 | zh_TW |
| dc.description.abstract | Nowadays, a lot of people choose to book their hotel via online. For those who use online booking are paying more attention to online ratings and reviews. That means all of ratings and reviews are very important to customers and operators. Customers will giveratings and write reviews according to experience of different aspects, for example, Tripavisor provides 6 different aspects for customers to evaluate. Meanwhile, text reviews can describe the experience of accommodation with more details. Therefore, this studyuses information of hotels in Taipei area to classify reviews by topic characteristics and sentiment analysis through text analysis, explore the relationship between ratings and reviews, and find out other potential aspects from reviews. In addition, the prospect theory mentioned that if there were same level of profit and loss, it would generate irrational thoughts that causing more impact of loss. This study will use the impact of reviews against positive and negative sentiment score to verify. The results of the study shows that reviews will affect the oriented ratings given by customers, while LDA used to find out two aspects from the reviews, which is food and environmental facilities. This result proves that while there is a lack of other type of aspects, we still can understand its orientation and feelings through reviews. In addition to the original six aspects, operators can also improve food and environment if they want to improve the accommodation experience. Furthermore, it proves that the influence of positive and negative sentiment scores on ratings is asymmetrical, and the result has again verified the prospect theory. From the perspective of customer ratings, the increment of negative sentiment will cause the customers to give less score. Therefore, if operators want to improve their own ratings, they should pay attention and deal with negative reviews more than positive reviews. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T16:02:09Z (GMT). No. of bitstreams: 1 U0001-0302202112224500.pdf: 1812813 bytes, checksum: 354bb49cf5d166c8e9b2eef071fabf3d (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 i 誌謝 ii 摘要 iii Abstract iv 目錄 v 圖目錄 vii 表目錄 viii 第1章、緒論 1 第2章 文獻回顧 8 2.1飯店線上評論評等相關研究 8 2.1.1飯店線上評論 8 2.1.2飯店評等 9 2.2 評論主題特徵 10 2.2.1 特徵選取 10 2.2.2 主題模型 11 2.3 情感分析 13 第3章、研究方法 16 3.1 資料蒐集與前處理 17 3.1.1 資料蒐集 17 3.1.2資料前處理 18 3.2 資料主題特徵分類 20 3.2.1 面向分割法 20 3.2.2 潛在狄利克雷分佈 23 3.3 情感分析 26 3.3.1 情感分數 26 3.3.2程度詞、否定詞 28 3.3.3 情感分數之計算方法 29 3.4 多元迴歸模型 30 3.4.1 情感分數對面向評等之實證模型設定 37 3.4.2 潛在面向主題實證模型 37 3.4.3 正負情感分數實證模型 38 第4章、實證結果與分析 39 4.1 資料之敘述統計 39 4.2 主題特徵分類結果 41 4.3 LDA模型主題分類結果 44 4.4 情感分析結果 45 4.5 LDA潛在主題分類及情感分析結果 47 4.6 迴歸分析 51 4.6.1 情感分數對面向評等之實證結果 51 4.6.2 潛在面向主題實證結果 54 4.6.3 正負情感分數實證結果 61 第5章 結論與建議 65 5.1 結論 65 5.2 研究改進與限制 66 參考文獻 68 附錄 78 附表1 中研院斷詞詞性標記表 78 附表2 程度詞表 80 附表3 面向評等之估計結果 81 附表4 餐廳飲食影響總評等之估計結果（控制面向評等填答） 84 附表5 環境設備影響總評等之估計結果（控制面向評等填答） 92 附表6 正、負情感分數對總評等之估計結果 100 附表7 正、負情感分數對面向評等之估計結果 101 | |
| dc.language.iso | zh-TW | |
| dc.title | 線上評論對消費者各項面向評等之相關性研究：以 Tripadvisor 為例 | zh_TW |
| dc.title | The relationship between aspect ratings and consumers' reviews: A case study of Tripadvisor | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 109-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 葉家瑜(Chia-Yu Yeh),陳淑玲(Shu-Ling Chen) | |
| dc.subject.keyword | 文本分析,面向分割法,潛在狄利克雷分配,情感分析,展望理論,多元迴歸, | zh_TW |
| dc.subject.keyword | Text Mining,Aspect Segmentation Algorithm,Latent Dirichlet Allocation,Sentiment Analysis,Prospect Theory,Multiple Linear Regression, | en |
| dc.relation.page | 103 | |
| dc.identifier.doi | 10.6342/NTU202100441 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2021-02-07 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農業經濟學研究所 | zh_TW |
| 顯示於系所單位： | 農業經濟學系 | |
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