臺灣博碩士論文加值系統

自德國飛船股份有限公司於1909年成立為全球第一家航空公司起，航空產業從載運乘客到目的地，陸續擴增各項服務，而乘客選擇航空公司的項目也從搭乘增加到票價、座椅、機上娛樂、餐點、機艙環境等各項目，因此航空產業的競爭越來越激烈。
隨著網際網路的興起，由實際體驗得出的電子口碑對消費者有著莫大的影響力，因此乘客在線上的評論對於航空公司有著極大的重要性。航空公司愈來愈重視旅客的需求，網路評論對於潛在購買旅客的決策也有重要的影響，因為從有體驗經驗的旅客所撰寫的網路評論被認為是客觀和可靠的訊息。
而Skytrax是來自英國針對航空公司的專業調查網站，乘客根據自身的搭乘經驗並自行登入Skytrax網站填寫問卷涵蓋350家航空公司，並依此產出最佳航空公司、機場等獎項。
本研究以星空聯盟、寰宇一家、天合聯盟旗下的航空公司為對象，擷取Skytrax的評論針對三大聯盟中各家航空公司做分析，以TF-IDF、關聯分析、情緒分析為研究的方法，顯示乘客對於三大聯盟之航空公司的推薦評論及不推薦評論，以作為航空公司提升服務的建議。

Since the establishment of Deutsche Bahn AG as the world's first airline in 1909, the airline industry has expanded its services from carrying passengers to their destinations, and passengers' choices of airlines have increased from flying to fares, seats, in-flight entertainment, meals, cabin environment, etc. As a result, competition in the airline industry has become increasingly fierce. With the rise of the Internet, electronic word-of-mouth from real-world experiences has a significant impact on consumers, so online passenger reviews are of great importance to airlines. As airlines increasingly focus on the needs of their passengers, online reviews have an important impact on the decisions of potential buyers, as they are considered to be objective and reliable information written by passengers who have experienced them. Skytrax is a professional survey website for airlines from the UK. Passengers fill in questionnaires based on their own flying experience and log in to Skytrax website, covering 350 airlines, and the best airlines and airports are awarded accordingly. This study takes the airlines of Star Alliance, OneWorld and SkyTeam as the target, and extracts Skytrax reviews to analyze each airline in the three alliances, using TF-IDF, association analysis and sentiment analysis as the research methods to show passengers' positive and negative reviews of the airlines in the three alliances, in order to make suggestions for airlines to improve their services.

目錄
論文摘要： I
ABSTRACT II
目錄 III
表次 V
圖次 VI
第一章、緒論 1
第一節、研究背景 1
第二節、研究動機 4
第三節、研究目的 5
第二章、文獻探討 7
第一節、網路口碑 7
第二節、網路爬蟲 9
第三節、文字探勘 11
第四節、TF-IDF分析 13
第五節、關聯分析 14
第六節、情緒分析 16
第七節、航空業線上評論 18
第三章、研究方法 19
第一節、研究設計 19
第二節、研究對象與樣本 20
第三節、資料分析方法與分析工具 22
第四節、研究架構 23
第五節、操作型定義 23
第四章、研究分析 24
第一節、資料蒐集 24
第二節、文字處理 24
第三節、TF-IDF分析 24
一、星空聯盟詞頻統計 25
（一）、星空聯盟推薦評論 25
（二）、星空聯盟不推薦評論 27
二、寰宇一家詞頻統計 29
（一）、寰宇一家推薦評論 29
（二）、寰宇一家不推薦評論 30
三、天合聯盟詞頻統計 32
（一）、天合聯盟推薦評論 32
（二）、天合聯盟不推薦評論 34
第四節、關聯分析 36
一、星空聯盟關聯分析 36
（一）、星空聯盟推薦評論 36
（二）、星空聯盟不推薦評論 38
二、寰宇一家關聯分析 40
（一）、寰宇一家推薦評論 40
（二）、寰宇一家不推薦評論 42
三、天合聯盟關聯分析 44
（ㄧ）、天合聯盟推薦評論 45
（二）、天合聯盟不推薦評論 47
第五節、情緒分析 49
一、星空聯盟 情緒分析 49
（一）、星空聯盟推薦評論情緒 49
（二）、星空聯盟不推薦評論情緒 49
二、寰宇一家 情緒分析 50
（一）、寰宇一家推薦評論情緒 50
（二）、寰宇一家不推薦評論情緒 51
三、天合聯盟 情緒分析 51
（一）、天合聯盟推薦評論情緒 51
（二）、天合聯盟不推薦評論情緒 52
第五章、結論 53
第一節、研究結果 53
第二節、研究限制與未來研究建議 55
參考文獻 57
英文文獻 57


表次
表4-1星空聯盟推薦評論之關鍵字 25
表4-2星空聯盟不推薦評論之關鍵字 27
表4-3寰宇一家推薦評論之關鍵字 29
表4-4寰宇一家不推薦評論之關鍵字 31
表4-5天合聯盟推薦評論之關鍵字 32
表4-6天合聯盟不推薦評論之關鍵字 34
表4-7星空聯盟推薦評論之關聯詞組 37
表4-8星空聯盟不推薦評論之關聯詞組 39
表4-9寰宇一家推薦評論之關聯詞組 41
表4-10寰宇一家不推薦評論之關聯詞組 43
表4-11天合聯盟推薦評論之關聯詞組 45
表4-12天合聯盟不推薦評論之關聯詞組 47


圖次
圖2-1聚焦網路爬蟲流程 11
圖 3-1研究方法之流程圖 20
圖3-2擷取SKYTRAX評論資料 22
圖3-3本研究之研究架構圖 23
圖4-1星空聯盟推薦評論之文字雲 27
圖4-2星空聯盟不推薦評論之文字雲 28
圖4-3寰宇一家推薦評論之文字雲 30
圖4-4寰宇一家不推薦評論之文字雲 32
圖4-5天合聯盟推薦評論之文字雲 34
圖4-6天合聯盟不推薦評論之文字雲 35
圖4-7星空聯盟推薦評論之字詞關聯網絡圖 38
圖4-8星空聯盟不推薦評論之字詞關聯網絡圖 40
圖4-9寰宇一家推薦評論之字詞關聯網絡圖 42
圖4-10寰宇一家不推薦評論之字詞關聯網絡圖 44
圖4-11天合聯盟推薦評論之字詞關聯網絡圖 46
圖4-12天合聯盟不推薦評論之字詞關聯網絡圖 48
圖4-13星空聯盟推薦評論之圓餅圖 49
圖4-14星空聯盟不推薦評論之圓餅圖 50
圖4-15寰宇一家推薦評論之圓餅圖 50
圖4-16寰宇一家不推薦評論之圓餅圖 51
圖4-17天合聯盟推薦評論之圓餅圖 52
圖4-18天合聯盟不推薦評論之圓餅圖 52

英文部分

Arndt, J. (1967). Role of Product-Related Conversations in the Diffusion of a New Product. Journal of Marketing Research, 4(3), 291-295.

Aureli, S. (2017). A Comparison of Content Analysis Usage and Text Mining in CSR Corporate Disclosure. The International Journal of Digital Accounting Research, 17, 1-32.

Agrawal, R, & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proceedings of the 20th International Conference on Very Large Data Bases, 94, 487-499.

Ayat, A. Z. (2020). Significant Labels in Sentiment Analysis of Online Customer Reviews of Airlines. Sustainability, 12(20), 8683.

Ban, J. H., & Kim, H. S. (2019). Understanding Customer Experience and Satisfaction through Airline Passengers’ Online Review. Sustainability, 11(15), 4066.

Bickart, B., & Schindler, R. M. (2001). Internet Forums as Influential Sources of Consumer Information. Journal of Interactive Marketing, 15(3), 31-40.

Blackwell, R. D., Miniard, P. W., & Engel, J. F. (2001). Consumer Behavior (9th ed.). San Diego, CA: Harcourt, Inc.

Chowdhury, G. G. (2010). Introduction to modern information. London: Facet .11, p. 508.

Claudia, D. (2021). Aviation World Rethinking Strategies after COVID-19 Crises. INCAS Bulletin, Bucharest 13(1), 247-256.

Chan, C. H., Chu, T. H., Proty, J. H., & Wen, T. H. (2021). Spatially Characterizing Major Airline Alliances: A Network Analysis. ISPRS International Journal of Geo-Information, Basel 10 (1), 37.

Dahlen, M., & Fredrik, L. (2005). Advertising Weak and Strong Brands: Who Gains? Psychology & Marketing, 22(6), 473-488.

Das, D. D., Sharan, S., Shubham, N., Neelu, K., & Brijendra, S. (2017). Sentimental Analysis for Airline Twitter Data. IOP Conference Series. Materials Science and Engineering, Bristol 263(4).

Deniz, S., Huseyin, A., & Saydam, M. B. (2022). Air-Travelers’ Perceptions of Service Quality during the COVID-19 Pandemic: Evidence from Tripadvisor Sites. Sustainability, 14(1), 435.

Findawati, Y., Hikmah, N. I., Sumarno, S., & Rachmawati, Y. (2021). HPAI Consumer Shopping Analysis Using Apriori Algorithm. IOP Conference Series. Materials Science and Engineering, 1098(3).

Gelb, B. D., & Suresh, S. (2002). Adapting to “Word of Mouse.” Business Horizons, 45 (4), 21-25.

Gelb, B., & Johnson, M. (1995). Word-of-Mouth Communications: Causes and Consequences. Journal of Health Care Marketing. 15, 54-58.

Haywood, K.M. (1989). Managing Word of Mouth Communications. Journal of
Services Marketing, 3(2), 55-67.

Herr, P. M., Kardes, F. R., & Kim, J. (1991). Effects of word-of-mouth and product-attribute information on persuasion: An accessibility-diagnosticity perspective. Journal of Consumer Research, 17(3), 455-462.

Inzalkar, S. & Sharma, J. (2015). A survey on text mining-techniques and application.
International Journal of Research in Science & Engineering, 24, 1-14.

Ju, S. E., Park, J., & Choi, Y. J. (2020). The Effect of Social Media Usage Characteristics on E-WOM, Trust, and Brand Equity: Focusing on Users of Airline Social Media, Sustainability, 12(4), 1691.

Justin, B., Jodi, M., Kalyssa, N., & Bradley, W. (2015). The Impact of Willingness to Engage in Negative Electronic Word-of-Mouth on Brand Attitude: A Study of Airline Passengers in South Africa. Journal of Business and Retail Management Research, 9(2), 68-84.

Jun, W., Xing, P., Ligeng, W., & Wei, W. (2018). Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation. Mathematical Problems in Engineering, 2018(1), 1-10.

Kwon, H. J., Jun, J. K., & Kim, H. S. (2021). Topic Modeling and Sentiment Analysis of Online Review for Airlines. Information, 12(2), 78.

Kim, Y., & Park, J. (2017). A Study on the Impact of Online Word-of-Mouth. for Airlines on Customer Behavior. The Open Transportation Journal, 11(1), 81-89.

Kim, Y. J., & Kim, H. S. (2022). The Impact of Hotel Customer Experience on. Customer Satisfaction through Online Reviews. Sustainability, 14(2), 848.

Kabir, H. (2016). Data Mining Framework for Generating Sales Decision Making Information Using Association Rules. International Journal of Advanced Computer Science and Applications, West Yorkshire, 7(5), 378-385.

Lutz, R. J. (1975). Changing Brand Attitudes Through Modification of Cognitive Structure. Journal of Consumer Research, 1(4), 49-59.

Mizerski, R. W. (1982). An Attribution Explanation of the Disproportionate. Influence of Unfavorable Information. Journal of Consumer Research, 9 (3), 301-310.

Michael S.(2000). Global Airline Alliances--Reaching out to New Galaxies in a Changing. Competitive Market-the Star Alliance and Oneworld. Journal of Air Law and Commerce, 65(2): 313-325.

Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. Proceedings of the 2nd international conference on knowledge capture (K-CAP 2003), Sanibel Island, FL, USA.

Nigel, E. (2001) Collaborative strategy: An analysis of the changing world of international airlines alliances. Tourism Management, 22(3), 229-243.

Oum T.H. and Park, J.H., (1992). Airline Alliance: Current Status, Policy Issues,
and Future Directions. Journal of Air Transport Management, 3(3), 133-144.

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2): 1-135.

Qian, Z. (2022). Personalized Hybrid Recommendation for Tourist Users Based on Matrix Cluster Apriori Mining Algorithm. Recent Advances in Industrial Mathematics and Applications 2021, 2022.

Ronen, F., & Ido, D. (1995). Knowledge discovery in textual databases (KDT). KDD, 95, 112-117.

Rida, K., & Siddhaling, U. (2018). Airline Sentiment Visualization, Consumer Loyalty Measurement and Prediction Using Twitter Data. International Journal of Advanced Computer Science and Applications. West Yorkshire, 9(6), 380-388.

Shadiyar, A, Ban, J. H., & Kim, S. H. (2020). Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis. Sustainability, 12(21), 9188.

Srikant, R., Quoc, V., & Agrawal, R. (1997). Mining Class-Association Rules with Constraints. Advances in Intelligent Systems and Computing, 2 307-318.

Seo, E. J., & Park, J. W.,(2018). A Study on the Influence of the Information Characteristics of Airline Social Media on E-WOM, Brand Equity and Trust. Open Transportation Journal, Sharjah. 12, 289-300.

Seo, G. H., & Munehiko, I. (2020). Perceptions of Customers as Sustained Competitive Advantages of Global Marketing Airline Alliances: A Hybrid Text Mining Approach. Sustainability, 12(15), 6258.

Sachin, K., & Mikhail, Z. (2019). A Machine Learning Approach to Analyze Customer Satisfaction from Airline Tweets. Journal of Big Data, Heidelberg, 6(1), 1-16.
Shuwei, X., & Weiqin, T. (2021). Prediction of User Consumption Behavior Data Based on the Combined Model of TF-IDF and Logistic Regression. Journal of Physics: Conference Series, Bristol, 1757(2021).

Sreeja, I, Joel, S. V., & Jatian, L. (2020). Twitter Sentiment Analysis on Airline Tweets in India Using R Language. Journal of Physics: Conference Series, 1427(1).

Sundaram. D. S., Mitra, K., and Webster, C. (1998). Word-of-mouth communications: A motivational analysis. Advances in Consumer Research, 25(1), 527-531.

Sullivan, D. (2001). Document warehousing and text mining: techniques for improving business operations, marketing, and sales: John Wiley & Sons, Inc (1th).

Shadiyar, A., Ban, H. J., & Kim, H. S. (2020). Extracting Key Drivers of Air Passenger’s Experience and Satisfaction through Online Review Analysis. Sustainability, 12(21) 9188.

Thorsten, H. T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004). Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing, 18(1), 38-52.

Yamg, L., Cindy, N., & Wenze, L. (2020). Changing Trends of Corporate Social Responsibility Reporting in the World-Leading Airlines. PLoS One, San Francisco , 15(6).

Ye, Q., Law, R., & Gu, B. (2009). The impact of online user reviews on hotel room
sales. International Journal of Hospitality Management, 28(1), 180-182.

Özlem, G., Hakan, A., & Ali, Ç. (2022). Netflix Verileri Üzerinde TF-IDF Algoritması ve Kosinüs Benzerliği Ile Bir İçerik Öneri Sistemi Uygulaması. AJIT-e, Istanbul , 13(48), 31-52.