臺灣博碩士論文加值系統

隨著網際網路快速發展，社群媒體使用者大幅增加，遊客透過網路評論蒐集旅遊資訊，做出旅遊決策，因此了解網路評論的內容對於活動推廣與滿足遊客需求相當重要。台灣燈會作為一個大型節慶活動，每年帶來龐大的觀光效益，過去研究較少使用社群媒體評論的內容，探討遊客對台灣燈會看法，故本研究蒐集Instagram有關台灣燈會的4944則貼文，藉由文字探勘了解2023台灣燈會遊客討論內容。研究結果顯示，2023台灣燈會遊客最重視的燈會內容是主燈，並且有許多關聯詞組與「國父紀念館」有關；評論以正面評論為主；貼文內容提及景點對貼文的按讚數有顯著影響。貼文內容共分為12個主題，高討論度的主題為作品及評價、整體燈會形象與未來展區作品；低討論度主題為藝術入店、表演團隊與作品展演。根據研究結果提出相關建議，以供日後辦理活動參考。

The Internet is developing rapidly and the number of social media users is growing fast. Tourists use online reviews to collect travel information and make travel decisions. It is important to understand the content of online reviews if the event wants to be promoted successfully and tourists' needs are to be met. The Taiwan Lantern Festival is a major event that brings great benefits to the tourism industry every year. Previous studies have rarely used the content of social media comments to explore tourists' views on the Taiwan Lantern Festival. In this study, we collected 4,944 Instagram posts about the Taiwan Lantern Festival and used text mining to understand the content of the discussions among tourists about the 2023 Taiwan Lantern Festival. The results of this study show that the main lantern was the most important feature of the 2023 Taiwan Lantern Festival. Many comments also referenced the Dr. Sun Yat-Sen Memorial Hall, and the comments were positive overall. The mention of scenic spots had a significant effect on the number of likes. The content of the posts was categorized into 12 topics. The most discussed topics were works and opinions, the overall image of the festival lanterns, and future works in the exhibition area. The least discussed topics were art in the store, performance teams, and the light show. Based on the results of the study, recommendations were made for future events.

摘要...........................................................................i
Abstract......................................................................ii
誌謝.........................................................................iii
目錄..........................................................................iv
表目錄........................................................................vi
圖目錄.......................................................................vii
第一章 緒論....................................................................1
1.1 研究背景與動機..............................................................1
1.2 研究目的...................................................................2
1.3 研究範圍...................................................................3
1.4 研究流程...................................................................4
第二章 文獻回顧.................................................................5
2.1 節慶活動...................................................................5
2.1.1 節慶活動的定義............................................................5
2.1.2 節慶活動的類型............................................................5
2.1.3 燈會相關研究..............................................................6
2.2 網路評論...................................................................9
2.2.1 網路評論的定義 ...........................................................9
2.2.2 網路評論相關研究 .........................................................9
2.3 文字探勘..................................................................11
2.3.1 文字探勘的定義...........................................................11
2.3.2 文字探勘的資料預處理.....................................................11
2.3.3 文字探勘的分析方法.......................................................13
2.3.4 文字探勘相關研究.........................................................14
第三章 研究方法................................................................16
3.1 研究架構..................................................................16
3.2 研究範圍與對象.............................................................17
3.2.1 研究範圍................................................................18
3.2.2 研究對象................................................................18
3.3 資料蒐集與清理.............................................................19
3.3.1 資料蒐集................................................................19
3.3.2 資料清理................................................................20
3.3.3 資料描述................................................................22
3.4 中文斷詞與資料轉換.........................................................23
3.4.1 建立燈會詞庫.............................................................23
3.4.2 中文斷詞................................................................24
3.4.3 詞頻統計與過濾...........................................................26
3.4.4 資料轉換................................................................28
3.5 文字探勘操作方式...........................................................28
3.5.1 詞頻-逆向檔案頻率(TF-IDF) ...............................................29
3.5.2 關聯分析................................................................29
3.5.3 情感分析................................................................30
3.5.4 主題模型................................................................31
第四章 資料分析與結果..........................................................33
4.1 關鍵字與文字雲.............................................................33
4.2 關聯分析..................................................................35
4.3 情感分析..................................................................36
4.3.1 中立評論................................................................36
4.3.2 正面評論................................................................38
4.3.3 負面評論................................................................39
4.3.4 情感傾向與時間分布.......................................................40
4.4 迴歸分析..................................................................41
4.5 主題分析..................................................................44
第五章 結論與建議..............................................................48
5.1 研究結論..................................................................48
5.2 研究建議..................................................................50
5.2.1 後續研究建議.............................................................50
5.2.2 相關單位建議.............................................................50
5.3 研究限制..................................................................51
參考文獻......................................................................52
附錄一........................................................................59
附錄二........................................................................60
附錄三........................................................................65
附錄四........................................................................67
Extended Abstract.............................................................69

[1]于健、歐韋伶（2021）。網路涉入、網路口碑與訊息信任對旅客行動上網產品購買決策影響之研究－歐洲地區為例。觀光與休閒管理期刊，9(2)，28-39。https://doi.org/10.6510/JTLM.202112_9(2).0004
[2]王芊昀（2023）。以文字探勘技術探討社群媒體使用者之容貌焦慮。南臺科技大學碩士論文。
[3]王貿（2020）。公務人員關注議題之文字探勘：以PTT公職板為例。調查研究-方法與應用，(45)，119-154。https://doi.org/10.7014/TCYCFFYYY.202010_(45).0005
[4]台灣經濟新報(2023)。TESG事件雷達主題分析。資料來源：https://www.tejwin.com/insight/tesg-%E4%BA%8B%E4%BB%B6%E9%9B%B7%E9%81%94%E4%B8%BB%E9%A1%8C%E5%88%86%E6%9E%90/
[5]台灣網路資訊中心(2023)。2023台灣網路報告。資料來源：https://report.twnic.tw/2023/。
[6]交通部(2017)。璀璨28年，台灣燈會的轉型升級。資料來源：https://www.ey.gov.tw/Page/5A8A0CB5B41DA11E/62ae8047-aa6e-4447-980f-22637e6736b0
[7]交通部觀光局(2022)。2017-2021年臺灣旅遊狀況調查。資料來源：https://admin.taiwan.net.tw/FileUploadCategoryListC003340.aspx?CategoryID=7b8dffa9-3b9c-4b18-bf05-0ab402789d59。
[8]交通部觀光局(2023)。節慶活動。資料來源：https://www.taiwan.net.tw/m1.aspx?sNo=0000104
[9]吳宗瓊、朱育瑩、蔡秀玲(2004)。地方觀光行銷策略之探討－墾丁地區節慶活動與非活動期間之不同。觀光研究學報，10(4)，87-100。https://doi.org/10.6267/jtls.2004.10(4)6
[10]李右婷、吳偉文、曹中丞（2013）。大型節慶活動之成功要素與挑戰課題～以2013台灣燈會在新竹為例。東亞論壇，(479)，1-14。https://www.airitilibrary.com/Article/Detail?DocID=18173675-201303-201307180045-201307180045-1-14
[11]李君如（2010）。節慶活動品質、遊客滿意度及忠誠度之研究～以2008、2009台中燈會為例。觀光旅遊研究學刊，5(2)，29-50。https://doi.org/10.29689/JTTR.201012.0002
[12]李君如（2013）。臺灣燈會的展示構成、遊客價值對忠誠度及城市意象之影響～兼論居民與外地者的差異分析。建築與規劃學報，14(2&3)，83-108。
[13]李君如、陳佩君（2010）。從消費價值與活動內容探討節慶遊客滿意度～以2010年中台灣元宵燈會爲例。島嶼觀光研究，3(4)，51-72。https://doi.org/10.29859/JITR.201012.0003
[14]李奇叡（2023，2月3日）。史上最貴台灣燈會拚211億元觀光產值。中國時報。資料來源：https://www.chinatimes.com/newspapers/20230203000466-260107?chdtv
[15]李明宗(2010)。台灣節慶活動的形貌－休閒社會學觀點的詮釋。運動文化研究，(13)，85-110。https://doi.org/10.29818/ss.201006.0003
[16]李政峰、施鴻琳、林柏宏（2020）。燈會節慶城市意象之研究。休閒事業研究，18(3)，1-8。https://doi.org/10.6746/LIR.202009_18(3).0001
[17]李政峰、曾干育、李世昌(2020)。台灣燈會城市意象、遊憩吸引力與認同感之相關研究。休閒事業研究, 18(1)，39-62。
[18]卓騰語言科技(2020)。中文NLP也需要處理停用詞 (Stop Words) 嗎？。資料來源：https://blog.droidtown.co/post/619240422284328960/tfidf。
[19]周世玉、蕭家旗、陳麒文、陳苡廷（2010）。體驗行銷對節慶活動形象及重遊意圖影響之探討－以臺中元宵燈會為例。企業管理學報，(85)，47-70。https://www.airitilibrary.com/Article/Detail?DocID=10259627-201006-201009270046-201009270046-47-70
[20]林士彥、張峻嘉、郭幸福、劉紹德（2022）。燈會節慶活動的天候狀況對遊客人潮估計之分析探討。休閒與遊憩研究，12(2)，28-44。https://doi.org/10.6157/ALRR.202212_12(2).0003
[21]林彣珊、唐嘉鴻(2010)。網路口碑說服效果之研究。行銷評論，7(2)，187-208。https://doi.org/10.29931/mr.201006.0003
[22]林育則、陳奕奇(2015)。正負面網路口碑對購買意願之影響：探討產品特性與產品涉入之調節效果。Electronic Commerce Studies，13(2)，167-193。
[23]林孟龍、張皓為、張宗政(2019)。以文字探勘技術探討旅遊地意象的探索研究：以日本環球影城為例。真理觀光學報，(15)，35-49。 https://doi.org/10.6438/tojt.201906_(15).0003
[24]社群實驗室(2023)。元宵節賞燈必看！網路熱議前六大熱門燈會排行出爐。資料來源：https://www.social-lab.cc/2023/02/trending-pressrelease/%e5%85%83%e5%ae%b5%e7%af%80%e8%b3%9e%e7%87%88%e5%bf%85%e7%9c%8b%ef%bc%81%e7%b6%b2%e8%b7%af%e7%86%b1%e8%ad%b0%e5%89%8d%e5%85%ad%e5%a4%a7%e7%86%b1%e9%96%80%e7%87%88%e6%9c%83%e6%8e%92%e8%a1%8c/
[25]施文妮、林裕森（2023）。應用PTT論壇文字探勘探討消費者對於網購平台的偏好之研究。International Journal of Systematic Innovation，7(5)，63-78。https://doi.org/10.6977/IJoSI.202303_7(5).0006
[26]柯瑞鈴（2017）。節慶活動對地方觀光產業之影響。臺灣經濟研究月刊，40(4)，112-119。https://doi.org/10.29656/TERM.201704.0017
[27]洪世章、賴俊彥、呂昀融(2022)。喜怒 (不) 形於色? 平台經濟的情感分析。Journal of Management & Business Research， (2521-4306)，39(2)。
[28]倪家珍、王笠言、林孟彥(2020)。住宿業訂房平台線上評論之比較性分析-以 Agoda 與 Airbnb 為例。觀光休閒學報，26(3)，259-282。https://doi.org/10.6267/JTLS.202012_26(3).0002
[29]徐淑如、董和昇、柳雅婷(2011)。網路論壇口碑強度、雙面訊息與口碑順序對說服效果之影響－產品涉入之干擾效應。電子商務學報，13(1)，135-167。https://doi.org/10.6188/jeb.2010.12(4).03
[30]浦心蕙、蕭涵中（2012）。電子口碑訊息來源可信度對旅遊產品購買意圖之影響。行銷科學學報，8(2)，97-115。https://www.airitilibrary.com/Article/Detail?DocID=18166601-201212-201409300012-201409300012-97-115
[31]張哲維、鍾政偉（2018）。以永續觀點探討節慶活動遊客價值、滿意度與忠誠度關係之研究－以美濃客庄節慶活動為例。休憩管理研究，5(1)，61-78。
[32]張鉯崴、賴文仁、張志弘（2013）。台灣燈會遊客對地區觀光意象、遊覽滿意度與口碑推薦意願之研究－以2013年新竹縣為例。運動休閒餐旅研究，8(4)，106-130。https://doi.org/10.29429/JSLHR.201312_8(4).05
[33]莊鎧溫、張心維(2017)。影響旅客遊憩行為之研究～以2017雲林燈會為例。觀光與休閒管理期刊，5(1)，179-188。https://doi.org/10.6510/jtlm.5(1).15
[34]陳世榮(2015)。社會科學研究中的文字探勘應用: 以文意為基礎的文件分類及其問題。人文及社會科學集刊，27(4)，683-718。
[35]陳沛悌、陳甫鼎、裴蕾(2014)。節慶活動對地方行銷之研究。運動休閒餐旅研究，9(3)，23-38。
[36]陳宜檉、蕭雅文、林怡倩(2017)。旅遊社群網站網路口碑信任對旅遊商品購買意願影響之研究：延伸科技接受模式的理論觀點。觀光與休閒管理期刊，5(1)，28-41。https://doi.org/10.6510/jtlm.5(1).03
[37]陳勁甫、何宜澤(2005)。事件遊客旅遊動機、活動體驗品質、滿意度與行為意向之研究-以 2004 府城七夕國際藝術節爲例。餐旅暨家政學刊，2，2。
[38]陳昱儒（2019）。基於隱含狄利克雷分布進行開放式問卷之主題導向文字探勘。國立中央大學碩士論文。
[39]陳韋帆、古倫維（2018）。中文情感語意分析套件CSentiPackage發展與應用。圖書館學與資訊科學，44(1)，24-41。https://doi.org/10.6245/JLIS.201804_44(1).0002
[40]陳耀茂(2021)。資料探勘與顧客分析：Modeler應用。五南圖書出版。台北。
[41]黃俊堯、柳秉佑（2016）。消費者線上口碑與評論研究：國內外相關文獻回顧與討論。臺大管理論叢，26(3)，215-256。https://doi.org/10.6226/NTUMR.2016.MAY.R.25104-001
[42]楊婷媖、劉詩語（2024）。運用文字探勘探討臺灣金級環保飯店的評論：以Google評論為例。觀光與休閒管理期刊，12(1)，79-89。https://doi.org/10.6510/JTLM.202406_12(1).0008
[43]臺北市政府(2023)。【2023台灣燈會在台北–光源台北】。資料來源：https://artemperor.tw/tidbits/14075。
[44]趙芝良、李佳真(2021)。解析高美濕地目的地屬性與價值內涵：網路內容探勘策略。濕地學刊，10(1)，29-47。
[45]蔡文婷(2005)。以節慶為名—四大類組新節慶。資料來源：https://www.taiwan-panorama.com.tw/Articles/Details?Guid=849be340-747b-4770-826b-c03c21ff1e14&CatId=10&postname=%E4%BB%A5%E7%AF%80%E6%85%B6%E7%82%BA%E5%90%8D%E2%94%80%E2%94%80%E5%9B%9B%E5%A4%A7%E9%A1%9E%E7%B5%84%E6%96%B0%E7%AF%80%E6%85%B6
[46]蔡玲瓏(2020)。服務創新與節慶意象對遊客重遊意願之影響-以臺灣燈會為例。休閒產業管理學刊，13(1)，1-26.。
[47]鄭天明、吳國清、陳美存(2007)。不同參與經驗之遊客對展覽型節慶的主題意象、活動吸引力、滿意度與休閒效益之影響研究-以高雄燈會為例。旅遊管理研究，7(1)，31-54。
[48]鄭妃君、葉靜輝、林侑賢（2023）。運用文字探勘及問卷調查探討線上評論對消費者購買意願的影響。東吳經濟商學學報，(106)，41-63。https://www.airitilibrary.com/Article/Detail?DocID=02593769-N202306070014-00002
[49]鍾政偉（2014）。節慶活動認知、滿意度與忠誠度關係之研究－以2013高雄燈會藝術節為例。島嶼觀光研究，7(1)，26-53。https://www.airitilibrary.com/Article/Detail?DocID=20725388-201403-201404010030-201404010030-26-53
[50]鍾政偉、張哲維(2013)。以遊客觀點建構節慶活動關鍵成功因素之研究。Journal of Data Analysis，8(6)，137-164。https://doi.org/10.6338/jda.201312_8(6).0007
[51]Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A. R., Knox, K., Sarminah S., & Ibrahim, O. (2019). Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services, 51, 331-343. https://doi.org/10.1016/j.jretconser.2019.06.014
[52]Aramendia-Muneta, M. E., Olarte-Pascual, C., & Ollo-López, A. (2020). Key Image Attributes to Elicit Likes and Comments on Instagram. Journal of Promotion Management, 27(1), 50–76. https://doi.org/10.1080/10496491.2020.1809594
[53]Bigné, E., Oltra, E., & Andreu, L. (2019). Harnessing stakeholder input on Twitter: A case study of short breaks in Spanish tourist cities. Tourism Management, 71, 490-503. https://doi.org/10.1016/j.tourman.2018.10.013
[54]Buzova, D., Sanz-Blas, S., & Cervera-Taulet, A. (2019). ‘Tour me onshore’: understanding cruise tourists’ evaluation of shore excursions through text mining. Journal of Tourism and Cultural Change, 17(3), 356-373. https://doi.org/10.1080/14766825.2018.1552277
[55]Ckip(2019)。https://github.com/ckiplab/ckiptagger/wiki/Chinese-README#license
[56]DataReportal(2024)。DIGITAL 2024: TAIWAN。Retrieved from: https://datareportal.com/reports/digital-2024-taiwan
[57]Fang, B., Ye, Q., Kucukusta, D., & Law, R. (2016). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52, 498-506. https://doi.org/10.1016/j.tourman.2015.07.018
[58]Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research, 68(6), 1261-1270. https://doi.org/10.1016/j.jbusres.2014.11.006
[59]Getz, D. (2008). Event tourism: Definition, evolution, and research. Tourism management, 29(3), 403-428. https://doi.org/10.1016/j.tourman.2007.07.017
[60]Getz, D., & Page, S. J. (2016). Progress and prospects for event tourism research. Tourism management, 52, 593-631. https://doi.org/10.1016/j.tourman.2015.03.007
[61]Gupta, V., & Lehal, G. S. (2009). A survey of text mining techniques and applications. Journal of emerging technologies in web intelligence, 1(1), 60-76.
[62]He, W., Zha, S. H., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472. https://doi.org/10.1016/j.ijinfomgt.2013.01.001
[63]Hennig-Thurau, 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.
[64]Huang, J., Wang, C., Su, M., Dai, Q., & Bhuiyan, M. Z. A. (2018). Inspecting influences on likes and comments of photos in instagram. 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. 10.1109/SmartWorld.2018.00168.
[65]Huete-Alcocer, N. (2017). A Literature Review of Word of Mouth and Electronic Word of Mouth: Implications for Consumer Behavior. Frontiers in Psychology, 8, 4. https://doi.org/10.3389/fpsyg.2017.01256
[66]Kim, K., Park, O. J., Yun, S., & Yun, H. (2017). What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management. Technological Forecasting and Social Change, 123, 362-369. https://doi.org/10.1016/j.techfore.2017.01.001
[67]Kirilenko, A. P., Stepchenkova, S. O., & Dai, X. (2021). Automated topic modeling of tourist reviews: Does the Anna Karenina principle apply?. Tourism Management, 83, 104241. https://doi.org/10.1016/j.tourman.2020.104241
[68]Kirilenko, A. P., Stepchenkova, S. O., Kim, H., & Li, X. (2018). Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research, 57(8), 1012-1025.
[69]Lee, J., & Kyle, G. T. (2012). Recollection consistency of festival consumption emotions. Journal of Travel Research, 51(2), 178-190. https://doi.org/10.1177/0047287510394197
[70]Leung, D., Law, R., van Hoof, H., & Buhalis, D. (2013). Social Media in Tourism and Hospitality: A Literature Review. Journal of Travel & Tourism Marketing, 30(1-2), 3-22. https://doi.org/10.1080/10548408.2013.750919
[71]Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism management, 68, 301-323. https://doi.org/10.1016/j.tourman.2018.03.009
[72]Li, S., You, M. J., Li, D. W., & Liu, J. (2022). Identifying coal mine safety production risk factors by employing text mining and Bayesian network techniques. Process Safety and Environmental Protection, 162, 1067-1081. https://doi.org/10.1016/j.psep.2022.04.054
[73]Li, Y. R., Lin, Y. C., Tsai, P. H., & Wang, Y. Y. (2015). Traveller-generated contents for destination image formation: Mainland China travellers to Taiwan as a case study. Journal of Travel & Tourism Marketing, 32(5), 518-533. https://doi.org/10.1080/10548408.2014.918924
[74]Ma, L., & Lew, A. A. (2012). Historical and geographical context in festival tourism development. Journal of Heritage tourism, 7(1), 13-31. https://doi.org/10.1080/1743873X.2011.611595
[75]Mehraliyev, F., Kirilenko, A. P., & Choi, Y. (2020). From measurement scale to sentiment scale: Examining the effect of sensory experiences on online review rating behavior. Tourism Management, 79, 11. https://doi.org/10.1016/j.tourman.2020.104096
[76]Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert systems with applications, 40(10), 4241-4251. https://doi.org/10.1016/j.eswa.2013.01.019
[77]Nowacki, M., & Niezgoda, A. (2020). Identifying unique features of the image of selected cities based on reviews by TripAdvisor portal users. Scandinavian Journal of Hospitality and Tourism, 20(5), 503-519. https://doi.org/10.1080/15022250.2020.1833362
[78]Onan, A., Korukoglu, S., & Bulut, H. (2016). Ensemble of keyword extraction methods and classifiers in text classification. Expert Systems with Applications, 57, 232-247. https://doi.org/10.1016/j.eswa.2016.03.045
[79]Schuckert, M., Liu, X., & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5), 608-621. https://doi.org/10.1080/10548408.2014.933154
[80]Sonk, M., & Tunger, D. (2024). Trend mining with Orange - using topic modeling in futures research with the example of urban mobility. European Journal of Futures Research, 12(1), 7. https://doi.org/10.1186/s40309-024-00229-1
[81]Tao, Y., He, Z., Wu, G., & Shi, C. (2023). Are all tourism review information on the platforms equally useful?. Journal of Hospitality and Tourism Management, 57, 102-111. https://doi.org/10.1016/j.jhtm.2023.09.008
[82]Westbrook, R. A. (1987). Product/consumption-based affective responses and postpurchase processes. Journal of marketing Research, 24(3), 258-270.
[83]Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. https://doi.org/10.1016/j.tourman.2016.10.001
[84]Yin, S. H., Fu, C. Z., & Dai, G. Q. (2023). Exploring the Festival Attendees' Experiences on Social Media: A Study on the Guangzhou International Light Festival. Sage Open, 13(1), 16. https://doi.org/10.1177/21582440221145154