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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8334完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊裕澤(Yuh-Jzer Joung) | |
| dc.contributor.author | Wei-Hong Lin | en |
| dc.contributor.author | 林瑋鴻 | zh_TW |
| dc.date.accessioned | 2021-05-20T00:52:16Z | - |
| dc.date.available | 2020-08-06 | |
| dc.date.available | 2021-05-20T00:52:16Z | - |
| dc.date.copyright | 2020-08-06 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-03 | |
| dc.identifier.citation | 江穎 (2017)。<機器學習預測《歌手》決賽全排名>。檢自https://zhuanlan.zhihu.com/p/26883727(Jun.29,2020) 吳美慧 (2013)。《文章強度、部落客評價及推薦產品取得方式對購買意圖之影響》。國立中央大學資訊管理研究所碩士論文。 金蛋網路數位行銷 (2018)。〈口碑行銷｜餐廳美食爆紅關鍵：IG打卡+GOOGLE評論〉。檢自https://www.gemarketing.com.tw/article/wom/food-ig-google/ (Dec.23,2019) 馬千惠 (2012)。《網路打卡的口碑傳播效果對消費者購買決策影響之研究－以餐飲業為例》。國立中山大學傳播管理研究所碩士論文。 陳佳雯 (2017)。《探討美食圖片內容力及社群影響力對按讚行為之影響 — 以Instagram為例》。國立中山大學資訊管理學系研究所碩士論文。 陳美如、蔡精育、宋鎧、范錚強 (2012)。《線上口碑對消費者購買意圖之影響－網路論壇的實驗研究》。中山管理評論，第20卷，第2期。 游和正、黃挺豪、陳信希 (2012)。《領域相關詞彙極性分析及文件情緒分類之研究》。Computational Linguistics and Chinese Language Processing, 17(4),33-48。 鉅亨網新聞中心 (2015)。〈商戶點評網站Yelp遇困境：評分有時並不公正〉。檢自https://news.cnyes.com/news/id/483830 (Dec.19,2019) 結巴中文斷詞 (2016)。<結巴中文斷詞台灣繁體版本>。檢自https://github.com/ldkrsi/jieba-zh_TW#readme (Jun.29,2020) 痞客幫 (2020)。<痞客幫社群影響力>。檢自https://pixranking.events.pixnet.net/influence (Jun.29,2020) 愛食記 (2020)。<從超過 50,000 家精選餐廳中，探索您不知道的熱門美食>。檢自https://ifoodie.tw/ (Jun.29,2020) 廖敏惠 (2015)。《網路美食評論情緒分析之研究》。國立高雄餐旅大學台灣飲食文化所產業研究所碩士論文。 網路溫度計 (2019年12月19日)。〈美食網路口碑排行〉。檢自https://dailyview.tw/Top100/Topic/90?volumn=0 (Dec.19,2019) 網路溫度計 (2020)。〈網路溫度計網路口碑排行榜資訊〉。檢自 https://dailyview.tw/Top100/RankInfo (Jun.29,2020) 謝佩庭 (2014)。《基於使用者情緒關鍵詞彙之臉書粉絲專頁評論分類與評分系統》。國立交通大學多媒體工程研究所碩士論文。 戰爭熱誠 (2019)。< Python機器學習筆記：隨機森林算法>。檢自https://www.cnblogs.com/wj-1314/p/9628303.html (Jun.29,2020) 簡之文 (2012)。《部落格文章情感分析之研究》。淡江大學資訊管理學系碩士論文。 Caitlin (2019)。< INSTAGRAM新手入門：怎麼看後台的數據分析>。檢自https://caitlin1010.pixnet.net/blog/post/311663027 (Jun.29,2020) HackMD (2020)。<用 Markdown 即時協作知識庫>。檢自https://hackmd.io/ (Jun.29,2020) INSTAGRAM (2020)。<在 Instagram 查看洞察報告>。檢自https://www.facebook.com/help/instagram/1533933820244654?helpref=uf_permalink (Jun.29,2020) MENU美食誌 (2020)。<吃貨必備，簡單記錄，搜尋好吃店家的美食神器>。檢自https://menutaiwan.com/tw/about (Jun.29,2020) Wikipedia (2020)。<皮爾遜積差相關係數>。檢自https://zh.wikipedia.org/wiki/%E7%9A%AE%E5%B0%94%E9%80%8A%E7%A7%AF%E7%9F%A9%E7%9B%B8%E5%85%B3%E7%B3%BB%E6%95%B0 (Jun.29,2020) An, J.X., Huang, J., Yu, W., Akoglu, L., Chandy, R., Faloutsos, C.(2011). Algorithm of Disambiguation and Matching of Chinese Word Segmentation in Connected Strategies Research. Advanced Materials Research (Volumes 219-220), 1702-1706. Bhasin, H. (2019). Retrieved from What is Hashtag Marketing? Importance Of Hashtag Marketing https://www.marketing91.com/what-is-hashtag-marketing/ (Dec 23,2019) Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. Brown, D., Hayes, N. (2008). Influencer Marketing: Who Really Influences Your Customers?, Routledge. Chaovalit, P., Zhou, L. (2005). Movie review mining: a compareson between supervised and unsupervised. In Proceedings of the 38th Hawaii International Conference on System Sciences. Chevalier, J. A., Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 345–354 Dai, W., Jin, G.Z., Lee, J., Luca, M. (2018). Aggregation of Consumer Ratings: An Application to Yelp.com. Quantitative Marketing and Economics, 16(3), 289-339 Dewey, J., Wheeler, J., (2009). Interest and Effort in Education. Southern Illinois University Press: eBook Academic Collection. Domingos, P., Richardson, M. (2002). Mining the network value of customers. In Proceeding of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 57–66. Gan, Q., Ferns, B.H., Yu, Y., Jin, L. (2017) A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews. Journal of Quality Assurance in Hospitality Tourism, 465-492 GONG, X. (2014). Strategic Customer Engagement on Instagram- A Case of Global Business to Customer (B2C) Brands. Master’s Thesis in Media Management, Media Management Master Program KTH Royal Institute of Technology. Jia, S. (2018). Behind the ratings: Text mining of restaurant customers’online reviews, International Journal of Market Research, 60(6), 561–572. Jin, J., Ji, P., Liu, Y. (2014). Recommending Rating Values on Reviews for Designers. Encyclopedia of Business Analytics and Optimization. Kamal, A. (2015). Review Mining for Feature Based Opinion Summarization and Visualization. International Journal of Computer Applications. 119(17) Kaviya, K., Roshini, C., Vaidhehi, V., Dhalia Sweetlin, J. (2017) Sentiment Analysis for Restaurant Rating. IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 140-145. Kempe, D., Kleinberg, J., Tardos, E. (2003). Maximizing the spread of influence through a social network. In the Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 137-146. Kralj Novak, P., Smailovic, J., Sluban, B., Mozetic, I. (2015). Sentiment of Emojis. PLoS ONE 10(12): e0144296. Lee K, D. (2015). Analytics, Goals, and Strategy for Social Media. Library Technology Reports, 51(1), 26–32. Ling Hang Yew, R., Binti Suhaidi, S., Seewoochurn, P., Kumar Sevamalai, V. (2018). Social Network Influencers’Engagement Rate Algorithm Using Instagram Data. 2018 Fourth International Conference on Advances in Computing, Communication Automation (ICACCA). Luca, M., Zervas, G. (2016). Fake It Till You Make It: Reputation, Competition, and Yelp Review Fraud. Management Science, 62(12), 3412-3427.https://doi.org/10.1287/mnsc.2015.2304 Luca, M. (2016). Reviews, Reputation, and Revenue: The Case of Yelp.com. Harvard Business School Working Paper, No. 12-016 Mir Riyanul Islam. (2014). Numeric Rating of Apps on Google Play Store by Sentiment Analysis on User Reviews. International Conference on Electrical Engineering and Information Communication Technology, pp.1-4. Moon, S., Bergey, P. K., Iacobucci, D. (2010). Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. Journal of Marketing, 74, 108–121. Murphy, R. (2018). Comparison of Local Review Sites: Which Platform is Growing the Fastest? Retrieved from https://www.brightlocal.com/research/comparison-of-local-review-sites/ (Dec 23,2019) Page, L., Brin, S., Motwani, R., Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Technical Report. Stanford InfoLab. Perera, I.K.C.U., Caldera, H.A. (2017). Aspect Based Opinion Mining on Restaurant Reviews. 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), 542-546. Peters, K., Chen, Y., Kaplan, A., Ognibeni, B., Pauwels, K. (2013). Social Media Metrics–A Framework and Guidelines for Managing Social Media. Journal of Interactive Marketing, 27, 281–298. Pitman, J.(2019).The Ultimate Guide to Google My Business Reviews. Retrieved from https://www.brightlocal.com/learn/how-do-google-reviews-work/ (Dec 23,2019) Rachid, A.D., Abdellah, A., Belaid, B., Rachid, L. (2018). Clustering Prediction Techniques in Defining and Predicting Customers Defection: The Case of E-Commerce Context. International Journal of Electrical and Computer Engineering (IJECE). Richardson, M., Domingos, P. (2002). Mining Knowledge-Sharing Sites for Viral Marketing. In Proceeding of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. Shih-Ming, W., Lun-Wei, K. (2016). ANTUSD：A Large Chinese Sentiment Dictionary. Language Resources and Evaluation Conference(LREC). Yang, L., Jian-Wu, B., Zhi-Ping, F. (2017). Ranking products through online reviews:A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Information Fusion, 36, 149-161. Yelp Elite Squad. (2014). Yelp Elite Squad. Retrieved from http://www.yelp.com/elite (Dec 23,2019) Zizzi, Hosie, R. (2017). HOW INSTAGRAM HAS TRANSFORMED THE RESTAURANT INDUSTRY FOR MILLENNIALS. Retrieved from https://www.independent.co.uk/life-style/food-and-drink/millenials-restaurant-how-choose-instagram-social-media-where-eat-a7677786.html (Dec 23,2019) Zhang, K., Cheng, Y., Liao, W.K., Choudhary, A. (2011). Mining millions of reviews:a technique to rank products based on importance of reviews. Proceedings of the 13th International Conference on Electronic Commerce, ACM. Zhu, T., Wu, B., Wang, B. (2009). Social Influence and Role Analysis Based on Community Structure in Social Network. Advanced Data Mining and Applications, 788-795. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8334 | - |
| dc.description.abstract | 隨著網際網路的發達，各種社群平台如雨後春筍般的興起，如：facebook、instagram、ppt、twitter、dcard...等，大眾僅需透過手機連上網即可隨時隨地發表自己的評論意見，討論著每一件事、每一個事物，因此對於美食愛好者而言，能夠透過大眾的意見找到真正的美食更是省下了自行搜尋的麻煩，也因此關於美食的評分與評論機制變的更加普及，如：google評論、愛食記、大重點評網、yelp、menu美食誌...等，然而，在眾多被推薦的店家中，該如何快速選擇最想造訪的店家儼然變成了一道難題，此時綜合大眾評價的店家排名即顯得相當重要。 因此本篇論文欲以公認排名推薦較準的社群平台為標準，設計餐飲評分機制學習此社群平台排名的計算方式，日後不再需要透過大眾投票的問卷調查或是美食社群的調查便能找出符合大眾心中的最佳餐廳排行。 本研究利用社群聆聽(social listening)的概念結合情緒分析技術，分析Instagram社群上用戶對於100間店家的評論資料，以公認推薦排名較準的menu美食誌top10餐廳排行榜，作為本研究餐飲評分機制的模型訓練資料，嘗試各種方式預測出趨近於menu美食誌top10的餐廳排名，並進一步探究預測排名準確或不準確之原因，最後驗證排名預測的準確度。 | zh_TW |
| dc.description.abstract | As the continuous development of the Internet, a variety of social media have been generated, such as Facebook, Instagram, PTT, twitter, Dcard and so on. People can give any opinion and discuss everything anywhere and anytime via their cellphones. Therefore, with regard to gastronomes, being able to utilize the public’s opinions to discover a real great delicacy will save their time to look for by themselves and that is the reason why rating systems concerning delicacies, such as reviews of Google Maps, Ifoodie app, Dianping, Yelp, MENU app, have become more and more popular. However, among large amounts of recommended stores, how to rapidly select one store people most want to visit has become a problem. Thus, ranks of stores considering the public’s reviews appear to be very important. Consequently, this thesis will regard ranks of stores people trust as standard ranks to design a rating system in catering industry. In this way, people can easily find out a good rank of stores recommended by the public and they do not have to conduct a survey of the public’s preferences about restaurants or wait for publications of any rank of stores. This thesis will take advantage of the concept of social listening and sentiment analysis to analyze reviews about 100 stores in Instagram. Additionally, this thesis will use ranks of stores of MENU app people highly regard as the training data of the rating system designed by this thesis and try different methods to predict the ranks of stores of MENU app. Furthermore, this thesis will also study why the prediction is accurate or inaccurate and verify the accuracy of the prediction. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T00:52:16Z (GMT). No. of bitstreams: 1 U0001-0308202016530300.pdf: 4727634 bytes, checksum: 4d952342ffbf46d077ce5e3abaf0130c (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 論文口試委員審定書…………………………………………………………….…i 誌謝………………………………………………………….………….….…….…ii 中文摘要…………………………………………………..……………..……...….iii Abstract…………………………………………………….……………..……...…iv 圖目錄………………………………………………………………………….……v 表目錄……………………………………………………………………………...vii 第一章、緒論 1 1.1 研究背景 1 1.2 研究動機 2 1.3 研究目的 3 1.4 論文架構 4 第二章、文獻探討 5 2.1 線上評分機制 5 2.1.1 Google map評分 6 2.1.2 Yelp 7 2.1.3 大眾點評網 8 2.1.4 評分機制比較 9 2.1.5 MENU美食誌 10 2.2 評論探勘 11 2.2.1 情緒分析相關研究 12 2.2.2 現有評分機制問題 14 2.3 Instagram社群行銷 16 2.3.1 主題標籤行銷 16 2.3.2 網路打卡行銷 17 2.4 影響力分析 17 2.4.1 社群影響力計算方式 18 2.4.2 社群影響力與排名之關係 20 2.4.3 社群影響力結合情緒分數與排名之關係 20 2.5 小結 21 第三章、研究方法 23 3.1 研究問題 23 3.2 研究架構 23 3.2.1 資料蒐集 26 3.2.2 Ad hoc模型與機器學習分類演算法 29 3.3 研究驗證 33 第四章、研究結果 35 4.1 資料爬取與分析 35 4.2 排名預測與驗證 39 4.2.1 Ad hoc之餐飲評分機制 39 4.2.2 機器學習分類演算法 55 4.2.3 Ad hoc方式與機器學習分類法之結果比較 63 4.3 各餐飲種類的排名差異分析 63 4.3.1 Ad hoc方式的排名結果分析 64 4.3.2 機器學習分類演算法的排名結果分析 65 4.4 小結 65 第五章、結論 68 5.1 研究成果 68 5.2 研究貢獻 69 5.3 研究限制 70 5.4 未來研究方向 71 中文參考文獻 72 西文參考文獻 75 附錄 80 附錄一、Ad hoc方式之排名結果 80 一、義大利麵 80 二、牛肉麵 80 三、滷肉飯 81 四、咖哩料理 81 五、港式餐廳 82 六、韓式炸雞 82 七、牛排 83 八、鐵板燒 83 九、冰品 84 十、韓式料理 84 附錄二、 擴增情緒詞典 85 | |
| dc.language.iso | zh-TW | |
| dc.title | 以Instagram資料實做一套餐飲評分機制 | zh_TW |
| dc.title | On the Use of Instagram Data for Building a Rating System in Catering Industry | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曹承礎(Seng-Cho Chou),盧信銘(Hsin-Min Lu),孔令傑(Ling-Chieh Kung) | |
| dc.subject.keyword | 情緒分析,社群聆聽,線上評分機制,評論探勘, | zh_TW |
| dc.subject.keyword | sentiment analysis,social listening,online rating system,review mining, | en |
| dc.relation.page | 97 | |
| dc.identifier.doi | 10.6342/NTU202002293 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2020-08-04 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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| U0001-0308202016530300.pdf | 4.62 MB | Adobe PDF | 檢視/開啟 |
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