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研究生:謝知晏
論文名稱:應用文字探勘技術於線上課程之口碑特徵分析
論文名稱(外文):Applying Text Mining in Online Course Review Analysis
指導教授:蘇宏仁蘇宏仁引用關係
指導教授(外文):Hung-Jen Su
口試委員:李佩璇艾昌瑞
口試日期:2021-07-27
學位類別:碩士
校院名稱:國立中正大學
系所名稱:企業管理系行銷管理研究所
學門:商業及管理學門
學類:行銷與流通學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:60
中文關鍵詞:電子口碑文字探勘線上課程
外文關鍵詞:eWOMtext miningonline course
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隨著網際網路與資訊科技的發展與普及,全球資料量急速攀升,資料總量增至 44 ZB。近年線上教育產業日漸成熟,2019年底爆發的Covid-19新冠疫情更加速此產業的發展,如何有效的運用非結構化資料於行銷領域,從海量資料中找出具價值的洞悉日顯重要。

本研究運用文字探勘技術來了解消費者評論,並蒐集台灣最大線上課程網站「Hahow」的使用者生成內容進行分析。藉由詞頻與Word2Vec了解消費者對於不同領域的偏好特徵,如:發現購買語言課程的消費者期待能夠達到專業水準,而購買設計課程的消費者則看重講師教學的步驟與細節等。運用LDA主題模型了解消費者的評論面向,主要分為講師的表達能力、講師的教學方法、課程的難易度、學生的期望與行為、課程的豐富度、課程是否符合需求、學生的學習感受。

分析結果可以讓線上課程的網站與講師,更精準掌握消費者於不同課程類型的偏好,以及消費者整題評論面向,可作為未來廣告關鍵字、廣告內容與改善策略的參考,在課程推廣與改善課程品質上有相當程度的貢獻。

With the development of information technology and the increasing prevalence of the internet, the amount of data worldwide has reached 44 ZB. Accelerated by the Covid-19 outbreak at the end of 2019, the online education industry has matured in recent years,
This study used text mining techniques to collect and analyze consumer reviews and user-generated content from Hahow, one of the online course websites in Taiwan. Word frequency and Word2Vec are used to understand consumers' preferences in different fields such as consumer behavior. For example, consumers who purchased language courses expect to achieve professional standards, while consumers who purchase design courses value the steps and details of the instructor's teaching. The LDA Topic Model is used to analyze the different dimensions of consumers' comments. This study found consumers' comments can be broken down into the following dimensions: instructor's presentation skills, instructor's teaching methods, levels of courses, students' expectations and behaviors, class content, and students' learning experience.
The results of the analysis will enable online course websites and instructors to have a more in-depth understanding of consumer preferences, which can be used as a reference for future advertising keywords, and hence make a considerable contribution to course promotion and the improvement of course quality.

第壹章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究流程 3
第貳章 文獻探討 4
第一節 線上課程 4
第二節 電子口碑 7
第三節 文字探勘 9
第參章 研究方法 11
第一節 研究架構 11
第二節 資料蒐集 12
第三節 資料預處理 13
第四節 資料分析方法 15
第肆章 研究結果 19
第一節 消費者評論之詞頻分析 19
第二節 消費者詞頻之關聯詞分析 26
第三節 消費者評論之主題分析 39
第伍章 結論與建議 45
第一節 研究發現與結論 45
第二節 管理意涵 47
第三節 研究限制與建議 48
參考文獻 49
一、中文文獻
李沃牆 (2020) 新冠肺炎引爆全球 [宅經濟] 商機. 會計研究月刊(413), 15-20林文寶林文寶
周君倚, & 陸洛 (2014) 以科技接受模式探討數位學習系統使用態度-以成長需求為調節變項. Information Management, 21(1), 83-106。
林文寶, & 吳淑靖 (2010) 影響線上學習市場購買行為因素之研究。台灣銀行季刊,62(3),173-201。
楊錦生, 謝佩芸, & 施曉萍 (2017) 社群媒體中顧客知識之挖掘: 意見探勘技術開發. 臺大管理論叢, 27(2S), 1-28。
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。
陳欣蘭 (2006) 從諾爾斯的自我導向學習看成人學習者在創意教學中的學習成效. 明新學報。

二、英文文獻
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of management education, 24(1), 32-54.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management science, 57(8), 1485-1509.
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of marketing research, 4(3), 291-295.
Atapattu, T., Falkner, K., & Tarmazdi, H. (2016). Topic-Wise Classification of MOOC Discussions: A Visual Analytics Approach. International Educational Data Mining Society.
Awad, N. F., & Ragowsky, A. (2008). Establishing trust in electronic commerce through online word of mouth: An examination across genders. Journal of management information systems, 24(4), 101-121.
Bi, J.-W., Liu, Y., Fan, Z.-P., & Zhang, J. (2019). Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews. Tourism Management, 70, 460-478.
Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of interactive marketing, 15(3), 31-40.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993-1022.
Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer research, 14(3), 350-362.
Burns, J., Clift, J., & Duncan, J. (1991). Understanding of understanding: Implications for learning and teaching. British Journal of Educational Psychology, 61(3), 276-289.
Chapman, C. (2020). Commentary: Mind your text in marketing practice. Journal of Marketing, 84(1), 26-31.
Chen, Y., & Xie, J. (2008). Online consumer review: Word-of-mouth as a new element of marketing communication mix. Management science, 54(3), 477-491.
Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of marketing research, 43(3), 345-354.
Collins, C., Hasan, S., & Ukkusuri, S. V. (2013). A novel transit rider satisfaction metric: Rider sentiments measured from online social media data. Journal of Public Transportation, 16(2), 2.
Crow, H., Gage, H., Hampson, S., Hart, J., Kimber, A., Storey, L., & Thomas, H. (2002). Measurement of satisfaction with health care: Implications for practice from a systematic review of the literature. Health technology assessment.
Dave, K., Lawrence, S., & Pennock, D. M. (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Paper presented at the Proceedings of the 12th international conference on World Wide Web.
Dellarocas, C. (2003). The digitization of word of mouth: Promise and challenges of online feedback mechanisms. Management science, 49(10), 1407-1424.
Dellarocas, C., & Narayan, R. (2006). A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statistical science, 21(2), 277-285.
Elgesem, D., Steskal, L., & Diakopoulos, N. (2015). Structure and content of the discourse on climate change in the blogosphere: The big picture. Environmental Communication, 9(2), 169-188.
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Filak, V. F., & Sheldon, K. M. (2008). Teacher support, student motivation, student need satisfaction, and college teacher course evaluations: Testing a sequential path model. Educational Psychology, 28(6), 711-724.
Hanson, W. (2000). Principles of Internet Marketing. Ohio: South-Western. In: College Publishing.
He, W., Tian, X., Tao, R., Zhang, W., Yan, G., & Akula, V. (2017). Application of social media analytics: A case of analyzing online hotel reviews. Online Information Review.
Heng, Y., Gao, Z., Jiang, Y., & Chen, X. (2018). Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services, 42, 161-168.
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.
Huang, L., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84-91.
Jin, Y. (2017). Development of word cloud generator software based on python. Procedia engineering, 174, 788-792.
Kanada, Y. (1999). A method of geographical name extraction from Japanese text for thematic geographical search. Paper presented at the Proceedings of the Eighth International Conference on Information and Knowledge Management.
Kiecker, P., & Cowles, D. (2002). Interpersonal communication and personal influence on the Internet: A framework for examining online word-of-mouth. Journal of Euromarketing, 11(2), 71-88.
Kim, S., & Hovy, E. (2004). Determining the Sentiment of Opinions In: Proceedings of the 20th International Conference on Computational Linguistics (COLING'04). In: Switzerland.
Kim, S. G., & Kang, J. (2018). Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews. Information processing & management, 54(6), 938-957.
Kirkpatrick, D., & Kirkpatrick, J. (2006). Evaluating training programs: The four levels: Berrett-Koehler Publishers.
Koltsova, O., & Shcherbak, A. (2015). ‘LiveJournal Libra!’: The political blogosphere and voting preferences in Russia in 2011–2012. New Media & Society, 17(10), 1715-1732.
Lee, W. S., & Liu, B. (2003). Learning with positive and unlabeled examples using weighted logistic regression. Paper presented at the ICML.
Li, L.-Y., & Tsai, C.-C. (2017). Accessing online learning material: Quantitative behavior patterns and their effects on motivation and learning performance. Computers & Education, 114, 286-297.
Liu, B. (2012). Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers 2012. In: ISBN 1–608–45884–9.
Liu, S., Ni, C., Liu, Z., Peng, X., & Cheng, H. N. (2017). Mining individual learning topics in course reviews based on author topic model. International Journal of Distance Education Technologies (IJDET), 15(3), 1-14.
Maskeri, G., Sarkar, S., & Heafield, K. (2008). Mining business topics in source code using latent dirichlet allocation. Paper presented at the Proceedings of the 1st India software engineering conference.
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404.
Nabli, H., Djemaa, R. B., & Amor, I. A. B. (2018). Efficient cloud service discovery approach based on LDA topic modeling. Journal of Systems and Software, 146, 233-248.
Nguyen, M.-T., Tran, D.-V., & Nguyen, L.-M. (2018). Social context summarization using user-generated content and third-party sources. Knowledge-Based Systems, 144, 51-64.
O’callaghan, D., Greene, D., Carthy, J., & Cunningham, P. (2015). An analysis of the coherence of descriptors in topic modeling. Expert Systems with Applications, 42(13), 5645-5657.
Packham, G., Jones, P., Miller, C., & Thomas, B. (2004). E‐learning and retention: Key factors influencing student withdrawal. Education+ Training.
Pavlou, P. A., & Dimoka, A. (2006). The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4), 392-414.
Peterson, K. D. (2000). Teacher evaluation: A comprehensive guide to new directions and practices: Corwin Press.
Ray, A., Bala, P. K., & Dwivedi, Y. K. (2021). Exploring values affecting e-Learning adoption from the user-generated-content: A consumption-value-theory perspective. Journal of Strategic Marketing, 29(5), 430-452.
Ridzuan, F., & Zainon, W. M. N. W. (2019). A review on data cleansing methods for big data. Procedia Computer Science, 161, 731-738.
Robinson, L. (2017). Embracing online education: Exploring options for success. Journal of Marketing for Higher Education, 27(1), 99-111.
Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. Paper presented at the Proceedings of the eighth ACM international conference on Web search and data mining.
Rose, J., & Lennerholt, C. (2017). Low cost text mining as a strategy for qualitative researchers. Electronic Journal of Business Research Methods, 15(1), 2-16.
Rubio, M. J. (2003). Focus and models of evaluations of the e-learning. RELIEVE Revista Electrónica de Investigación y Evaluación Educativa, 9(2).
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information processing & management, 24(5), 513-523.
Schuckert, M., Liu, X., & Law, R. (2015). A segmentation of online reviews by language groups: How English and non-English speakers rate hotels differently. International Journal of Hospitality Management, 48, 143-149.
Senaratne, H., Bröring, A., Schreck, T., & Lehle, D. (2014). Moving on Twitter: using episodic hotspot and drift analysis to detect and characterise spatial trajectories. Paper presented at the Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks.
Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Paper presented at the Proceedings of the workshop on interactive language learning, visualization, and interfaces.
Silverman, G. (1997). How to harness the awesome power of word of mouth. DIRECT MARKETING-GARDEN CITY-, 60, 32-37.
Smith, R. E., & Swinyard, W. R. (1982). Information response models: An integrated approach. Journal of Marketing, 46(1), 81-93.
Steyvers, M., Smyth, P., Rosen-Zvi, M., & Griffiths, T. (2004). Probabilistic author-topic models for information discovery. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
Sullivan, D. (2001). Document warehousing and text mining: techniques for improving business operations, marketing, and sales: John Wiley & Sons, Inc.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management science, 42(1), 85-92.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.
Wang, W., Feng, Y., & Dai, W. (2018). Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, 142-156.
Wyatt, J. C. (2000). When to use web-based surveys. In: BMJ Group BMA House, Tavistock Square, London, WC1H 9JR.
Yakout, M., Berti-Équille, L., & Elmagarmid, A. K. (2013). Don't be scared: use scalable automatic repairing with maximal likelihood and bounded changes. Paper presented at the Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data.
Yang, C. C., Tang, X., Wong, Y., & Wei, C.-P. (2010). Understanding online consumer review opinions with sentiment analysis using machine learning. Pacific Asia Journal of the Association for Information Systems, 2(3), 7.


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