基於機器學習之特徵選取方法於葡萄酒評論文本分類之研究

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：134

、訪客IP：13.59.67.238

姓名

呂浩瑜(Hao-Yu Lu) 查詢紙本館藏

畢業系所

資訊管理學系在職專班

論文名稱

基於機器學習之特徵選取方法於葡萄酒評論文本分類之研究
(Research on feature selection methods based on machine learning in wine review text classification)

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)

隨著網際網路和社群媒體的普及以及Covid-19疫情影響，葡萄酒消費者越來越依賴線上評論來決定購買選擇。本研究旨在比較不同文本特徵值萃取方法在葡萄酒評論文本分類中的效果，以期對葡萄酒評論文本分類技術的發展和應用做出貢獻並提高消費者在購買葡萄酒時的選擇效率。本研究首先從VIVINO葡萄酒評論網爬取1500則評論並請專家標記香氣與口感類別，經過資料預處理後，分別使用TF-IDF、Doc2vec和BERT-word embedding三種文本特徵選取方法產生字詞向量。接著搭配Naive Bayes、Logistic Regression、Random Forest、Support Vector Machine和XGBoost五種分類模型，探討不同的特徵表示法與分類器在文本分類中的表現和適用性。研究結果顯示，最適合本次紅酒資料集五個目標變數的模型組合皆為使用TF-IDF文字轉譯器搭配XGBoost分類模型，這種組合的預測準確率皆高於0.8，表現出色。此外，使用樣本合成法SMOTE來解決樣本不平衡問題時，模型的結果有小幅度提升，尤其是Accuracy與Precision。但當原始樣本過於龐大時，SMOTE可能不值得使用，因為需要耗費較多的時間處理資料不平衡，而僅能提升小幅度的效果。

摘要(英)

With the widespread use of the internet and social media, as well as the impact of the Covid-19 pandemic, wine consumers are increasingly relying on online reviews to make purchasing decisions. This study aims to compare the effectiveness of different text feature extraction methods in wine review text classification, in order to contribute to the development and application of wine review text classification techniques and improve the efficiency of consumers′ choices when purchasing wine. In this study, we first crawled 1,500 reviews from the VIVINO wine review website and asked experts to label aroma and taste categories. After data preprocessing, we used TFIDF, doc2vec, and BERT-word embedding methods to generate word vectors. We then paired these with five classification models, namely Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine, and XGBoost, to explore the performance and applicability of different feature representations and classifiers in text classification. The results showed that the most suitable model combination for the five target variables of this wine dataset was using the Tf-idf text transformer paired with the XGBoost classification model, which had a prediction accuracy of more than 0.8, demonstrating excellent performance. Moreover, when using the Synthetic Minority Over-sampling Technique (SMOTE) to address the issue of sample imbalance, there was a slight improvement in the model′s results, especially in terms of accuracy and precision. However, when the original sample size is too large, SMOTE may not be worth using, as it requires more time to process data imbalance and only results in a slight improvement in performance.

關鍵字(中)

★ 文本分類
★ 機器學習
★ 葡萄酒評論

關鍵字(英)

論文目次

誌謝 1
摘要 1
Abstract 1
圖目錄 1
表目錄 1
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 5
第二章文獻探討 6
2.1葡萄酒評論相關研究 6
2.2葡萄酒文本特徵值選取方法相關研究 9
第三章研究方法 14
3.1研究架構 14
3.2資料蒐集 15
3.3特徵工程 18
3.3.1目標變數標記 18
3.3.2清洗文字資料 19
3.4文字前處理方法 21
3.5分類方法 21
3.6實驗設計與指標評估 24
第四章實驗結果 27
4.1實驗結果 27
4.2實驗結果綜整 41
第五章結論與建議 43
5.1研究結論與貢獻 43
參考文獻 46

參考文獻

Acuti, D., Magherini, L., Mazzoli, V., Bandinelli, R., Donvito, R., Rinaldi, R., & Aiello, G. (2017). QR Code and the Wine Sector: What Contents? An Exploratory Research Study on the Wine Industry. In R. Rinaldi & R. Bandinelli (Eds.), Business Models and ICT Technologies for the Fashion Supply Chain (pp. 293–304). Springer International Publishing. https://doi.org/10.1007/978-3-319-48511-9_24
Baek, H., Ahn, J., & Choi, Y. (2012). Helpfulness of Online Consumer Reviews: Readers’ Objectives and Review Cues. International Journal of Electronic Commerce, 17(2), 99–126. https://doi.org/10.2753/JEC1086-4415170204
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bruwer, J., & Alant, K. (2009). The hedonic nature of wine tourism consumption: An experiential view. International Journal of Wine Business Research, 21(3), 235–257. https://doi.org/10.1108/17511060910985962
Chen, B., Rhodes, C., Crawford, A., & Hambuchen, L. (2014). Wineinformatics: Applying Data Mining on Wine Sensory Reviews Processed by the Computational Wine Wheel. 2014 IEEE International Conference on Data Mining Workshop, 142–149. https://doi.org/10.1109/ICDMW.2014.149
Chen, B., Velchev, V., Palmer, J., & Atkison, T. (2018). Wineinformatics: A Quantitative Analysis of Wine Reviewers. Fermentation, 4(4), Article 4. https://doi.org/10.3390/fermentation4040082
Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009). Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations. International Journal of Electronic Commerce, 13(4), 9–38. https://doi.org/10.2753/JEC1086-4415130402
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. https://doi.org/10.1509/jmkr.43.3.345
Chivu-Draghia, C., & Antoce, A. O. (2016). UNDERSTANDING CONSUMER PREFERENCES FOR WINE: A COMPARISON BETWEEN MILLENNIALS AND GENERATION X. 16(2).
Chung, C., & Muk, A. (2017). Online Shoppers’ Social Media Usage and Shopping Behavior. In C. L. Campbell (Ed.), The Customer is NOT Always Right? Marketing Orientationsin a Dynamic Business World (pp. 133–133). Springer International Publishing. https://doi.org/10.1007/978-3-319-50008-9_35
Croijmans, I., Hendrickx, I., Lefever, E., Majid, A., & Bosch, A. V. D. (2020). Uncovering the language of wine experts. Natural Language Engineering, 26(5), 511–530. https://doi.org/10.1017/S1351324919000500
Dong, Z., Guo, X., Rajana, S., & Chen, B. (2020). Understanding 21st Century Bordeaux Wines from Wine Reviews Using Naïve Bayes Classifier. Beverages, 6(1), Article 1. https://doi.org/10.3390/beverages6010005
Dubois, P., & Nauges, C. (2010). Identifying the effect of unobserved quality and expert reviews in the pricing of experience goods: Empirical application on Bordeaux wine. International Journal of Industrial Organization, 28(3), 205–212. https://doi.org/10.1016/j.ijindorg.2009.08.003
Forbes, D., Forbes, S. C., Blake, C. M., Thiessen, E. J., & Forbes, S. (2015). Exercise programs for people with dementia. Cochrane Database of Systematic Reviews, 4. https://doi.org/10.1002/14651858.CD006489.pub4
Grashuis, J., Skevas, T., & Segovia, M. S. (2020). Grocery Shopping Preferences during the COVID-19 Pandemic. Sustainability, 12(13), Article 13. https://doi.org/10.3390/su12135369
Hendrickx, I., Lefever, E., Croijmans, I., Majid, A., & van den Bosch, A. (2016). Very quaffable and great fun: Applying NLP to wine reviews. PROCEEDINGS OF THE 54TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2016), VOL 2, 306–312. http://hdl.handle.net/1854/LU-8071597
Hong, H., Xu, D., Wang, G. A., & Fan, W. (2017). Understanding the determinants of online review helpfulness: A meta-analytic investigation. Decision Support Systems, 102, 1–11. https://doi.org/10.1016/j.dss.2017.06.007
Horverak, Ø. (2009). Research Note—Wine Journalism—Marketing or Consumers’ Guide? Marketing Science, 28(3), 573–579. https://doi.org/10.1287/mksc.1090.0489
Katumullage, D., Yang, C., Barth, J., & Cao, J. (2022). Using Neural Network Models for Wine Review Classification. Journal of Wine Economics, 17(1), 27–41. https://doi.org/10.1017/jwe.2022.2
Kolb, D., & Thach, L. (2016). Analyzing German winery adoption of Web 2.0 and social media. Journal of Wine Research, 27(3), 226–241. https://doi.org/10.1080/09571264.2016.1190324
Kwabla, W., Coulibaly, F., Zhenis, Y., & Chen, B. (2021). Wineinformatics: Can Wine Reviews in Bordeaux Reveal Wine Aging Capability? Fermentation, 7(4), Article 4. https://doi.org/10.3390/fermentation7040236
Lefever, E., Hendrickx, I., Croijmans, I., Van den Bosch, A., & Majid, A. (2018, May 7). Discovering the Language of Wine Reviews: A Text Mining Account [Proceedings Paper]. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018); LREC. https://eprints.whiterose.ac.uk/137244/
Martinez, R. D., Angus, G., & Mahdavian, R. (2018). Grapevine: A Wine Prediction Algorithm Using Multi-dimensional Clustering Methods (arXiv:1807.00692). arXiv. https://doi.org/10.48550/arXiv.1807.00692
Masset, P., Weisskopf, J.-P., & Cossutta, M. (2015). Wine Tasters, Ratings, and En Primeur Prices*. Journal of Wine Economics, 10(1), 75–107. https://doi.org/10.1017/jwe.2015.1
McCannon, B. C. (2020). Wine Descriptions Provide Information: A Text Analysis. Journal of Wine Economics, 15(1), 71–94. https://doi.org/10.1017/jwe.2020.3
Mudambi, S. M., & Schuff, D. (2010). Research Note: What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly, 34(1), 185–200. https://doi.org/10.2307/20721420
Naujoks, A., & Benkenstein, M. (2020). Expert cues: How expert reviewers are perceived online. Journal of Service Theory and Practice, 30(4/5), 531–556. https://doi.org/10.1108/JSTP-11-2019-0240
Paradis, C., & Eeg-Olofsson, M. (2013). Describing Sensory Experience: The Genre of Wine Reviews. Metaphor and Symbol, 28(1), 22–40. https://doi.org/10.1080/10926488.2013.742838
Pucci, T., Casprini, E., Nosi, C., & Zanni, L. (2019). Does social media usage affect online purchasing intention for wine? The moderating role of subjective and objective knowledge. British Food Journal, 121(2), 275–288. https://doi.org/10.1108/BFJ-06-2018-0400
Ramirez, C. D. (2010). Do Tasting Notes Add Value? Evidence from Napa Wines*. Journal of Wine Economics, 5(1), 143–163. https://doi.org/10.1017/S1931436100001425
Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. (Kouros). (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7, 100216. https://doi.org/10.1016/j.trip.2020.100216
Thompson, G. M., & Mutkoski, S. A. (2011). Reconsidering the 1855 Bordeaux Classification of the Médoc and Graves using Wine Ratings from 1970–2005*. Journal of Wine Economics, 6(1), 15–36. https://doi.org/10.1017/S1931436100001048
Yang, C., Barth, J., Katumullage, D., & Cao, J. (2022). Wine Review Descriptors as Quality Predictors: Evidence from Language Processing Techniques. Journal of Wine Economics, 17(1), 64–80.
Yue, L., Liu, Y., & Wei, X. (2017). Influence of online product presentation on consumers’ trust in organic food: A mediated moderation model. British Food Journal, 119(12), 2724–2739. https://doi.org/10.1108/BFJ-09-2016-0421
Zhu, F., & Zhang, X. (Michael). (2010). Impact of Online Consumer Reviews on Sales: The Moderating Role of Product and Consumer Characteristics. Journal of Marketing, 74(2), 133–148. https://doi.org/10.1509/jm.74.2.133

指導教授

胡雅涵

審核日期

2023-6-15

推文