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    题名: 基於意見探勘與主題模型之部落格食記剖析研究
    A Study of Opinion Mining and Topic Model Analysis on Food Diaries
    作者: 賴柏帆
    Lai, Po Fan
    贡献者: 楊建民
    Yang, Jian Min
    賴柏帆
    Lai, Po Fan
    关键词: 意見探勘
    LDA
    主題模型
    餐廳評分
    Opinion Mining
    LDA
    Topic Model
    Restaurant Rating
    日期: 2016
    上传时间: 2016-07-20 17:15:26 (UTC+8)
    摘要: 隨著Web 2.0興起，社群網站在資訊傳遞與獲取所占比重相當高。以美食領域來看，人們在進餐廳前先行閱覽食記評論之情形越來越常見，而部落格文章因圖文並茂，常被消費者列入參考比較之來源。儘管這一類食記內容相對短篇食評來說較為完整，但評論分散於文章中，且多半沒有評分可供參考，讀者很難在第一時間獲悉評論樣貌，得花上一番心力進行閱覽，才能對餐廳整體有所評鑑。
    本研究提出一套基於意見探勘與主題模型的食記剖析方法，由部落格中各餐廳貼文情緒量來反映正負面評價，將提及評論歸納為「食物」、「服務」及「環境」三個評分面向，進而提供該家餐廳的整體推薦分數，供讀者快速參閱之。實驗語料自痞客邦美食類貼文中選定添好運台灣－台北站前店、京星港式飲茶PART2、金泰日式料理－內湖店以及喀佈貍（一店）大眾和風串燒居酒洋食堂，合計4家餐廳與200篇語料。
    透過LDA主題模型對食記敘述進行主題式分群，使擁有相近主題概念的句子分為一群，並歸類至各面向，例如喀佈貍（一店）之語料可分為10群主題語句，食物面向上有6群，服務與環境面向各為2群。另一方面，為了更有效辨別食記中含有的正負向情緒，本研究透過語意導向方法(SO-PMI)來計算食記中常出現情緒詞彙之極性，以建置該領域的意見詞詞庫。
    實驗結果方面，以線上餐廳評論網站－iPeen愛評網作為驗證對象，顯示其語料的平均情緒量相近，於大眾觀感與評價上傾向一致，且相較一般評論網站，本研究能從較細微的面向來切入，並以情緒量反映真實的餐廳評價。最後提出未來欲探討與改善之處，供後續研究參考之。
    As the time of Web 2.0 rise, social media platform plays a crucial role in transferring and receiving information. More and more people get used to reading the related posts before having meal. Because of its richness in content and referring photographs, blog posts are most frequently used for reference. Although the blog posts are more complete regarding their content than other short reviews, the actual reviews are scattered among words that are simply descriptions, and there are no grading scale to take as reference. These all together gives the reader a hard time to efficiently organize the overview of the review, and for them to, therefore, make the decision if they should go to the restaurant.
    Our study offers a method of analyzing food diaries based on opinion mining and topic model. The scale of emotion in a blog post about a restaurant is used as the reflection of its review`s positive or negative. The comments are categorized into food, service and environment. And the restaurant will be graded based on these three aspects to further provide the user an overall score of recommendation.
    We collected total of 200 articles written on 4 restaurants in PIXNET, then categorized the contents using LDA (Latent Dirichlet Allocation) model base on their theme. The sentences with similar theme with be put into a group, then be further categorized to the three aspects that was mentioned earlier. On the other hand, to better distinguish if the emotion in certain food diary is positive or negative, our study calculated the polarity of common opinion-based words in food diaries using semantic orientation (SO-PMI), and built an opinion corpus specifically for food diaries.
    In terms of the result, using iPeen, a restaurant rating website, as test reference, it shows that the average scales of opinion of the restaurants we got using our method are close to iPeen, which in this case we can say are close to the public opinion and review. Furthermore, compare to common rating website, our study touches on even the minute aspect, and use the cumulative opinion to reflect the true blog authors` evaluation of the restaurant. Lastly, we would like to bring up what we intend to discuss and improve in the future for upcoming research`s reference.
    參考文獻: 中文文獻
    吳佳芸。2015。應用探勘技術於社會輿情以預測捷運週邊房地產市場之研究。國立政治大學資訊管理研究所。
    吳權益。2013。以LDA為基礎之群組喜好文件推薦。國立中山大學資訊管理學系研究所。
    李珮瑩。2011。以資料探勘方法探討服務業之顧客區隔及滿意度指標-以大台北地區餐廳為例。國立台北科技大學資訊與運籌管理研究所。
    林政輝。2010。以口碑為基礎之個人化餐廳推薦機制。中原大學資訊管理研究所。
    林頌堅。2014。以主題模型方法為基礎的資訊計量學領域研究主題分析。教育資料與圖書館學，51(4)，499-523。
    邱鴻達。2010。意見探勘在中文電影評論之應用。國立交通大學資訊科學與工程研究所。
    洪崇洋。2012。以LDA和使用紀錄為基礎的線上電子書主題趨勢發掘方法。國立中山大學資訊管理學系研究所。
    康龍魁、王淑慧。2011。服務業實體環境對消費者行為之影響--以餐廳為例. 臺灣銀行季刊，62(1)，122-133。
    張日威。2014。應用LDA進行Plurk主題分類及使用者情緒分析。國立雲林科技大學資訊管理研究所。
    張育蓉。2012。使用情緒分析於圖書館使用者滿意度評估之研究。國立中興大學圖書資訊學研究所。
    張莊平。2012。中文文法剖析應用於電影評論之意見情感分類。國立臺灣師範大學資訊工程研究所。
    陳貴鳳、黃棣華。2010。台灣文化美食餐廳評鑑制度中評估指標之建構。餐旅暨家政學刊，7(3)，235-259
    游和正。2012。領域相關詞彙極性分析及文件情緒分類之研究。國立臺灣大學資訊工程學研究所。
    馮時、景珊、楊卓、王大玲。2013。基於 LDA 模型的中文微博話題意見領袖挖掘。東北大學學報 (自然科學版)，34(4)，490。
    葉剛、江夏寬等(2012)。台灣餐館評鑑。台北：二魚文化。
    鄒函升。2014。新聞輿情與民意偵測追蹤之研究-大資料之研究取向。國立政治大學資訊管理研究所。
    謝侑蓉。2011。食記分享攻防戰－談置入性行銷入侵美食部落格。國立高雄餐飲大學台灣飲食文化產業研究所。
    蘇靖淑。2007。中式速食連鎖餐廳消費者外食價值與消費知覺關係之研究。休閒暨觀光產業研究，2(1)，133-146。
    蘇靖淑、洪久賢。2009。米其林餐廳指南研究之回顧與近期發展。休閒暨觀光產業研究，4(1)，116-128。
    蘇靖淑、洪久賢。2010。美國 Zagat Survey 餐廳評鑑制度對台灣餐飲業發展之啟示。休閒暨觀光產業研究，2(1)，147-159。

    英文文獻
    Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993-1022.
    Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235.
    Hu, M., & Liu, B. (2004, July). Mining opinion features in customer reviews. InAAAI (Vol. 4, No. 4, pp. 755-760).
    Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. InProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177). ACM.
    Huang, Y. H., Pu, X. J., Yuan, C. F., & Wu, G. S. (2011). Appraisal expression extraction based on parse tree structure. Jisuanji Yingyong Yanjiu, 28(9), 3229-3234.
    Jo, Y., & Oh, A. H. (2011, February). Aspect and sentiment unification model for online review analysis. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 815-824). ACM.
    Johns, N., & Pine, R. (2002). Consumer behaviour in the food service industry: a review. International Journal of Hospitality Management, 21(2), 119-134.
    Kobayashi, N., Inui, K., & Matsumoto, Y. (2007). Opinion mining from web documents: Extraction and structurization. Information and Media Technologies,2(1), 326-337.
    Naveed, N., Gottron, T., Kunegis, J., & Alhadi, A. C. (2011, June). Bad news travel fast: A content-based analysis of interestingness on twitter. In Proceedings of the 3rd International Web Science Conference (p. 8). ACM.
    Newman, D., Hagedorn, K., Chemudugunta, C., & Smyth, P. (2007, June). Subject metadata enrichment using statistical topic models. In Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries (pp. 366-375). ACM.
    Pang, B., & Lee, L. (2005, June). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd annual meeting on association for computational linguistics (pp. 115-124). Association for Computational Linguistics.
    Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1-2), 1-135.
    Papadimitriou, C. H., Tamaki, H., Raghavan, P., & Vempala, S. (1998, May). Latent semantic indexing: A probabilistic analysis. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems (pp. 159-168). ACM.
    Park, C. (2004). Efficient or enjoyable? Consumer values of eating-out and fast food restaurant consumption in Korea. International Journal of Hospitality Management, 23(1), 87-94.
    Pettijohn, L. S., Pettijohn, C. E., & Luke, R. H. (1997). An evaluation of fast food restaurant satisfaction: determinants, competitive comparisons and impact on future patronage. Journal of Restaurant & Foodservice Marketing, 2(3), 3-20.
    Popescu, A. M., & Etzioni, O. (2007). Extracting product features and opinions from reviews. In Natural language processing and text mining (pp. 9-28). Springer London.
    Russel, B. (1975). Situational variables and consumer behavior. Journal of Consumer Research du mois de Décembre, 157-164.
    Su, Q., Xu, X., Guo, H., Guo, Z., Wu, X., Zhang, X., ... & Su, Z. (2008, April). Hidden sentiment association in chinese web opinion mining. In Proceedings of the 17th international conference on World Wide Web (pp. 959-968). ACM.
    Turney, P. D. (2002, July). Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417-424). Association for Computational Linguistics.
    Turney, P., & Littman, M. L. (2002). Unsupervised learning of semantic orientation from a hundred-billion-word corpus.
    Turney, P. D., & Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS), 21(4), 315-346.
    Zhuang, L., Jing, F., & Zhu, X. Y. (2006, November). Movie review mining and summarization. In Proceedings of the 15th ACM international conference on Information and knowledge management (pp. 43-50). ACM.
    描述: 碩士
    國立政治大學
    資訊管理學系
    103356027
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0103356027
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

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