運用文字探勘技術於網購評論分類－以Dcard為例

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：44

、訪客IP：18.188.211.58

姓名

林昀漢(Yun-Han Lin) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

運用文字探勘技術於網購評論分類－以Dcard為例
(Using text mining techniques to classify online shopping reviews – A case study of Dcard)

相關論文

★ 不動產仲介業銷售住宅類別之成交預測模型—以不動產仲介S公司為例	★ 應用文字探勘技術建構預測客訴問題類別機器學習模型
★ 以機器學習技術建構顧客回購率預測模型：以某手工皂原料電子商務網站為例	★ 以機器學習建構股價預測模型：以台灣股市為例
★ 以機器學習方法建構財務危機之預測模型：以台灣上市櫃公司為例	★ 運用資料探勘技術於股票填息之預測模型：以台灣股市上市公司為例
★ 運用資料探勘技術優化次世代防火牆規則之研究	★ 應用資料探勘技術於電子病歷文本中識別相關新資訊
★ 應用深度學習於藥品後市場監督：Twitter文本分類任務	★ 運用電子病歷與資料探勘技術建構腦中風病人心房顫動預測模型
★ 考量特徵選取與隨機森林之遺漏值填補技術	★ 電子病歷縮寫消歧與一對多分類任務
★ 運用Meta-path與注意力機制改善個人化穿搭推薦	★ 運用機器學習技術建構核保風險預測模型：以A公司為例
★ 風扇壽命預測使用大數據分析－以 X 公司為例	★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-7-1以後開放)

摘要(中)

在現代的社會中網路購物逐漸的普及，普遍群眾都有網路購物的經驗，而消費者在網路購物中可能會遇到許多問題，進而提出疑問甚至是產生客訴，隨著網際網路和社群媒體的發展，消費者開始在論壇、聊天室、Blog和社群媒體上分享產品和服務的使用體驗和意見，研究指出線上評論對消費者的決策有重大影響，並對產品銷售產生積極影響，了解消費者對網路購物平台的評價和意見對於企業改善服務品質和了解消費者需求至關重要，在Web 2.0時代，使用者生成內容就成為了重要資源，提供了消費者真實體驗和資訊交流的管道，也是一般企業在客戶服務管道無法取得足的資訊，因此主動搜集社群媒體上的資訊並進行系統化分類可以節省時間且蒐集更多資訊，利用社群媒體數據分析的方式，將網路論壇上的評論資料轉化為有關網路購物的有意義的知識，本研究抓取Dcard網路論壇之網路購物看板之資料作為研究中使用的資料集，將評論內容透過文字清洗、取得文件特徵，將非結構化資料轉變為可以使用的資料，研究中透過詞頻-逆文本頻率(Term Frequency-Inverted Document Frequency, TD-IDF)、Sentence-BERT (SBERT)以及fastText取得文件特徵，以及搜集台灣前五大網路購物平台之客服專區提供的問題，統計六種網路購物問題，再對評論內容進行分類的標記，最後實驗使用Orange3資料探勘工具，將三種文件特徵透過單一文件特徵以及組合文件特徵兩種方式實驗，透過工具內的五種監督式學習模型支援向量機(Support Vector Machine, SVM)、隨機森林(Random Forest, RF)、邏輯斯回歸(Logistic Regression, LR)、K-近鄰演算法(K-Nearest Neighbors, KNN)以及樸素貝葉斯(Naïve Bayes, NB)等五種較常使用在分類任務研究的演算法來對網路購物評論進行分類，研究結論指出，在使用單一文件特徵時以fastText方法使用LR模型表現最佳，而在使用組合文件特徵依然是LR模型表現最好，而研究發現在文件特徵部分組合中有混和fastText方法能夠有效提升模型效能及分類準確度。

摘要(英)

In modern society, online shopping has gradually become widespread, and the general public has experience with it. Consumers may encounter various problems while shopping online, leading to questions and even complaints. With the development of the Internet and social media, consumers have started sharing their product and service experiences and opinions on forums, chat rooms, blogs, and social media platforms. Studies have shown that online reviews have a significant impact on consumer decision-making and positively affect product sales. Understanding consumer evaluations and opinions on online shopping platforms is crucial for businesses to improve service quality and understand consumer needs. In the era of Web 2.0, user-generated content has become an important resource, providing a channel for consumers to share their real experiences and exchange information, which is often not accessible through traditional customer service channels. Proactively collecting and systematically categorizing information from social media can save time and gather more information. By analyzing social media data, online forum reviews can be transformed into meaningful knowledge about online shopping. This study uses data from the Dcard online forum′s online shopping board as the dataset, processing review content through text cleaning and extracting document features, transforming unstructured data into usable information. Term Frequency-Inverted Document Frequency (TD-IDF), Sentence-BERT (SBERT), and fastText are used to obtain document features, along with collecting questions provided by the customer service sections of the top five online shopping platforms in Taiwan. Six types of online shopping problems are identified, and review content is categorized accordingly. Orange3 data mining tool is used in the experiment, applying both single document features and combined document features approaches. Five supervised learning models, Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) are used for classification tasks. The study concludes that when using single document features, the best performance is achieved with the fastText method and the LR model, while the LR model still performs best when using combined document features. The study finds that incorporating the fastText method in the feature combination can effectively improve model performance and classification accuracy.

關鍵字(中)

★ 文字探勘
★ 網路購物
★ 評論分類
★ 監督式學習

關鍵字(英)

★ Text Mining
★ Online Shopping
★ Review Classification
★ Supervised Learning

論文目次

圖目錄 vi
表目錄 vii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
第二章文獻探討 6
2.1 使用者評論的影響 6
2.2 主題及評論分類任務 7
2.3 組合文件特徵 10
2.4 總結 11
第三章研究方法 12
3.1 研究架構 12
3.2 資料收集 13
3.3 資料前處理 14
3.4 文件特徵 19
3.5 分析工具 20
3.6 實驗設計與評估標準 23
第四章實驗結果 26
4.1 實驗一 26
4.2 實驗二 27
4.3 綜合討論 28
第五章結論 29
5.1 研究結論 29
5.2 研究限制 30
5.3 未來研究方向與建議 30
參考文獻 31
附錄 36

參考文獻

劉夢曉. (2017). 負面在線評論和商家反饋對消費者購買意願的影響研究. 電子商務學報, 19(1).
Anh, K. Q., Nagai, Y., & Nguyen, L. M. (2019). Extracting Customer Reviews from Online Shopping and Its Perspective on Product Design. Vietnam Journal of Computer Science, 06(01), 43–56.
Bahtar, A. Z., & Muda, M. (2016). The Impact of User – Generated Content (UGC) on Product Reviews towards Online Purchasing – A Conceptual Framework. Procedia Economics and Finance, 37, 337–342.
Bhatt, A., Patel, A., Chheda, H., & Gawande, K. (2015). Amazon Review Classification and Sentiment Analysis. 6.
Bickart, B., & Schindler, R. M. (2001). Internet forums as influential sources of consumer information. Journal of Interactive Marketing, 15(3), 31–40.
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, 5, 135–146.
Bouazizi, M., & Ohtsuki, T. (2018). Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer. IEEE Access, 6, 64486–64502.
Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511–521.
Chen, Y., Fay, S., & Wang, Q. (2011). The Role of Marketing in Social Media: How Online Consumer Reviews Evolve. Journal of Interactive Marketing, 25(2), 85–94.
Chen, Y., & Xie, J. (2008). Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix. Management Science, 54, 477–491.
Cheong, H. J., & Morrison, M. A. (2008). Consumers’ Reliance on Product Information and Recommendations Found in UGC. Journal of Interactive Advertising, 8(2), 38–49.
Cheung, C. M. K., & Lee, M. K. O. (2012). What drives consumers to spread electronic word of mouth in online consumer-opinion platforms. Decision Support Systems, 53(1), 218–225.
Chevalier, J. A., & Mayzlin, D. (2006). The Effect of Word of Mouth on Sales: Online Book Reviews. 43(3), 345–354.
Chong, A. Y. L., Ch’ng, E., Liu, M. J., & Li, B. (2017). Predicting consumer product demands via Big Data: The roles of online promotional marketing and online reviews. International Journal of Production Research, 55(17), 5142–5156.
Daud, S., Ullah, M., Rehman, A., Saba, T., Damaševičius, R., & Sattar, A. (2023). Topic Classification of Online News Articles Using Optimized Machine Learning Models. Computers, 12(1), 16.
Himelboim, I., Smith, M. A., Rainie, L., Shneiderman, B., & Espina, C. (2017). Classifying Twitter Topic-Networks Using Social Network Analysis. Social Media + Society, 3(1), 205630511769154.
Hu, N., Liu, L., & Sambamurthy, V. (2011). Fraud detection in online consumer reviews. Decision Support Systems, 50(3), 614–626.
Ibrahim, N. F., & Wang, X. (2019). A text analytics approach for online retailing service improvement: Evidence from Twitter. Decision Support Systems, 121, 37–50.
Inzalkar, S. M., & Sharma, J. (2015). A Survey on Text Mining- techniques and application. International Journal of Research In Science.
Karthikeyan, N., & Selva Brunda, S. (2019). Online Consumer Inspection on using E-shopping Service of E-commerce. International Journal of Recent Technology and Engineering, 8(2S11), 438–442.
Khan, A., Gul, M. A., Zareei, M., Biswal, R. R., Zeb, A., Naeem, M., Saeed, Y., & Salim, N. (2020). Movie Review Summarization Using Supervised Learning and Graph-Based Ranking Algorithm. Computational Intelligence and Neuroscience, 2020, 1–14.
Khedkar, S., & Shinde, S. (2020). Deep Learning and Ensemble Approach for Praise or Complaint Classification. Procedia Computer Science, 167, 449–458.
Krumm, J., Davies, N., & Narayanaswami, C. (2008). User-Generated Content. IEEE Pervasive Computing, 7(4), 10–11.
Lee, K., Palsetia, D., Narayanan, R., Patwary, Md. M. A., Agrawal, A., & Choudhary, A. (2011). Twitter Trending Topic Classification. 2011 IEEE 11th International Conference on Data Mining Workshops, 251–258.
Li, X., Wu, C., & Mai, F. (2019). The effect of online reviews on product sales: A joint sentiment-topic analysis. Information & Management, 56(2), 172–184.
Mudambi, S. M., & Schuff, D. (2010). What Makes a Helpful Review? A Study of Customer Reviews on Amazon.com (SSRN Scholarly Paper No. 2175066).
Nurfikri, F. S., Mubarok, M. S., & Adiwijaya. (2018). News Topic Classification Using Mutual Information and Bayesian Network. 2018 6th International Conference on Information and Communication Technology (ICoICT), 162–166.
Presi, C., Saridakis, C., & Hartmans, S. (2014). User-generated content behaviour of the dissatisfied service customer. European Journal of Marketing, 48(9/10), 1600–1625.
Rahman, A., & Akter, Y. A. (2019). Topic Classification from Text Using Decision Tree, K-NN and Multinomial Naïve Bayes.
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (arXiv:1908.10084). arXiv.
Resnick, P., & Zeckhauser, R. (2002). Trust among strangers in internet transactions: Empirical analysis of eBay’ s reputation system. In M. R. Baye (Ed.), The Economics of the Internet and E-commerce (Vol. 11, pp. 127–157). Emerald Group Publishing Limited. https://doi.org/10.1016/S0278-0984(02)11030-3
Rustam, F., Mehmood, A., Ahmad, M., Ullah, S., Khan, D. M., & Choi, G. S. (2020). Classification of Shopify App User Reviews Using Novel Multi Text Features. IEEE Access, 8, 30234–30244.
Santos, M. L. B. dos. (2021). The “so-called” UGC: An updated definition of user-generated content in the age of social media. Online Information Review, 46(1), 95–113.
Setiawan, E. B., Widyantoro, D. H., & Surendro, K. (2016). Feature expansion using word embedding for tweet topic classification. 2016 10th International Conference on Telecommunication Systems Services and Applications (TSSA), 1–5.
Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Kumar Roy, P. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.
Sunitha, D. C. K. (2018). Problems towards Online Shopping. 6(1).
Timoshenko, A., & Hauser, J. R. (2019). Identifying Customer Needs from User-Generated Content. Marketing Science, 38(1), 1–20.
Vangara, R. V. B., Thirupathur, K., & Vangara, S. P. (2020). Opinion Mining Classification using Naive Bayes Algorithm. International Journal of Innovative Technology and Exploring Engineering, 9(5), 495–498.
Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362–369.
Wu, I.-L. (2013). The antecedents of customer satisfaction and its link to complaint intentions in online shopping: An integration of justice, technology, and trust. International Journal of Information Management, 33(1), 166–176.

指導教授

胡雅涵(Ya-Han Hu)

審核日期

2023-6-20

推文