| 研究生: |
藍宇杰 Lan, Yu-Chieh |
|---|---|
| 論文名稱: |
以文字探勘分析消費者之照相偏好的線上評論 A Text Mining Approach to Investigate the On-line Reviews of Consumers' Preference of Photograph |
| 指導教授: |
呂執中
Lyu, Jr-Jung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 資訊管理研究所 Institute of Information Management |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 85 |
| 中文關鍵詞: | 文字探勘 、電子口碑 、TF-IDF 、相機 |
| 外文關鍵詞: | Text Mining, e-WOM, TF-IDF, Camera |
| 相關次數: | 點閱:267 下載:12 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
根據相機市場產業調查報告,因疫情影響造成相機市場在2020年跌至谷底,原因是疫情期間各國的鎖國政策,造成群眾無法外出旅遊進而影響到相機市場,並在疫情得到控制後,相機市場持續回暖、逐漸上升。消費者在選購相機的過程中容易受到口碑影響,且消費者因為自身的照相偏好與拍攝需求,在最終的選購上有所不同,也有消費者選擇手機取代照相機進行攝影,因此若能分析購買照相機之消費者的意見,企業就可加強區隔照相機與手機攝影之功能,增加潛在消費者與穩固現有消費者在專業照相領域的市場與口碑。
本研究旨在發展出一種整合性模型,藉由模型中的統計與計算程式,協助相機廠商快速找出疫情後相機市場的成功因素,整合性模型除了可進行一般的主題建模,利用調整模型內的參數,進行更深度的評論分析與研究,針對台灣兩大論壇ptt.cc與巴哈姆特中的相機討論版,共蒐集65921筆網友的留言進行分類,利用可信度分析針對個別網友的TF-IDF進行加權處理,找出高可信度網友的相機功能偏好。研究結果顯示,使用可信度分析在留言聚類任務中可改進約6%的聚類成效,能加強主題建模的精確程度,而評論中有兩的隱含主題,顯示相機產業的成功因素分別是「暗光拍攝 」、「對焦速度」兩個主要面向。使用本研究發展之整合性模型能準確的將顧客分群,且可根據需求更改加權變數進行更不同的分析面向,提供企業有價值的參考資訊。
After the pandemic was brought under control, the camera market gradually bounced back and its growth rate was much higher than expected. Since the people were getting used to on-line shopping during the pandemic, word-of-mouth could affect the sales volume significantly. Compared to smartphone, several features of camera had attracted the consumers to buy cameras for photography purpose. The intent of this work is to develop a framework in order to analyze the on-line opinions of consumers to identify the key features of popular cameras which differentiate them in the professional photography domain.
This study has developed an integrated model, which adopts credibility analysis and classification algorithms, to identify the key features of camera based on a total of 65,921 comments from camera discussion boards on two major forums - ptt.cc and Bahamut. Besides general topic modeling, in-depth review analysis and TF-IDF weighting analysis for individual users were applied. The research results show the camera feature preferences of highly credible users. The use of credibility analysis further improved clustering performance by about 6% in the comment clustering task which enhancing the precision of topic modeling. Two themes emerged from the reviews analysis: "low-light photography" and "focus speed." Through the proposed model developed in this study, manufacturers could develop cameras that could better meet the needs of professional photography.
中文文獻:
簡惠卿(2017)。智慧型手機照相功能影響消費意願關鍵因素之研究。醒吾科技大學行銷與流通管理系所碩士論文,新北市。
郭鑫華(2007)。照相手機與數位相機廠商競合策略之研究。國立臺灣大學工業工程學研究所碩士論文,台北市。
網路資料:
工研院 產科國際所https://ieknet.iek.org.tw/
經濟部加工出口區管理處https://www.epza.gov.tw/info.aspx?pageid=a875323238688970&cid=6f4f5cd47bdb2913
日本經濟新聞中文版https://zh.cn.nikkei.com/industry/itelectric-appliance/51289-2023-02-03-04-51-03.html
日本相機影像器材工業協會(CIPA)CIPA - Camera & Imaging Products Association: Statistics
英文文獻:
Afzal, M, Wai Wong, J. K. & Fard Fini, A. A.(2023) Unlocking Insights: Analysing Construction Issues in Request for Information (RFI) Documents with Text Mining and Visualisation. 2023 IEEE 19th International Conference on Automation Science and Engineering ,1-6, 10.1109.
Aizawa, A. (2003). An information-theoretic perspective of tf-idf measures. Information Processing & Management, 39(1), 45-65
Alachram, H., Chereda, H., Beißbarth, T., Wingender, E., & Stegmaier, P. (2021). Text mining-based word representations for biomedical data analysis and protein-protein interaction networks in machine learning tasks. PloS One, 16(10), e0258623.
Alamoudi, E. S., & Alghamdi, N. S. (2021). Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings. Journal of Decision Systems, 30(2-3), 259-281.
Aubaid, A. M., & Mishra, A. (2018). Text Classification Using Word Embedding in Rule-Based Methodologies: A Systematic Mapping. Tem Journal-Technology Education Management Informatics, 7(4), 902-914.
Bansal, H. S., & Voyer, P. A. (2000). Word-of-Mouth Processes within a Services Purchase Decision Context. Journal of Service Research, 3(2), 166-177.
Beldar, P., Pardeshi, M.R., Rakhade, R., & Mene, S. (2023). Analysis of Clusters With Indian Patent Data Using Different Word Embedding Techniques. Journal of Survey in Fisheries Sciences.9(13).92-108.
Bhat, M. R., Kundroo, M. A., Tarray, T. A., & Agarwal, B. (2020). Deep LDA : A new way to topic model. Journal of Information & Optimization Sciences, 41(3), 823-834.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3(4-5), 993-1022.
Bone, P. F. (1995). Word-of-Mouth Effects on Short-Term and Long-Term Product Judgments. Journal of Business Research, 32(3), 213-223.
Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. (2011). Customer Engagement: Conceptual Domain, Fundamental Propositions, and Implications for Research. Journal of Service Research, 14(3), 252-271.
Brooks, R. C. (1957). “Word-of-Mouth” Advertising in Selling New Products. Journal of Marketing, 22(2), 154-161.
Carroll, B.A., Ahuvia, A.C.(2006). Some antecedents and outcomes of brand love. Marketing Letter 17, 79–89.
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.
Dashrath, M. Gaurav, B. & Santosh, S.(2016). Text and keyword driven automation testing using selenium web driver. International Research Journal of Engineering and Technology, 3(7),23950056
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293-307.
Dong, M. Lu, J. Wang, G. Zheng, X. & Kiritsis, D.(2022). Model-based Systems Engineering Papers Analysis based on Word Cloud Visualization. 2022 IEEE International Systems Conference,1-7.
Dessí, D., Dragoni, M., Fenu, G., Marras, M., & Recupero, D. R. (2020). Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews. Deep Learning-Based Approaches for Sentiment Analysis,12(28).57-83.
Egger, R., & Yu, J. (2022). A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts. Frontiers in sociology, 7, 886498.
Floyd, K., Ling, R. F., Alhogail, S., Cho, H. Y., & Freling, T. (2014). How Online Product Reviews Affect Retail Sales: A Meta-analysis. Journal of Retailing, 90(2), 217-232.
García, B., Munoz-Organero, M., Alario-Hoyos, C. et al. (2021). Automated driver management for Selenium WebDriver. Empir Software Engineering 26, 107-121.
Hasan,S. A., Ruiqin, W. and Hussain, M. G., Clustering Analysis of Bangla News Articles with TF-IDF & CV Using Mini-Batch K-Means and K-Means,17-22, 10.1109.
HE, Z.(2021). Cross Platform Text Mining Based on Public Emergency—Using Word2vec Model and K-means Algorithm. IEEE International Conference on Artificial Intelligence and Computer Applications,2(31), 1-7.
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.
Jiao, C. N., Gao, Y. L., Yu, N., Liu, J. X., & Qi, L. Y. (2020). Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification. IEEE Journal of Biomedical and Health Informatics, 24(10), 3002–3011.
Levy, S. and Gvili, Y. (2015). How credible is e-word of mouth across digital-marketing channels?: theroles of social capital, information richness, and interactivity. Journal of Advertising Research. 55(1),95-109.
Li, A., Huang, X., Hao, B., O'Dea, B., Christensen, H., & Zhu, T. (2015). Attitudes towards suicide attempts broadcast on social media: An exploratory study of Chinese microblogs. PeerJ, 3, e1209.
Li, X. L., Zhang, X. X., Yuan, Y., & Dong, Y. S. (2022). Adaptive Relationship Preserving Sparse NMF for Hyperspectral Unmixing. Ieee Transactions on Geoscience and Remote Sensing, 60.59-77.
Liu, Q., Liang, Y., Wang, S., Huang, Z., Wang, Q., Jia, M., Li, Z., & Ming, W. K. (2022). Health Communication through Chinese Media on E-Cigarette: A Topic Modeling Approach. International journal of environmental research and public health, 19(13), 7591.
Liu, Z. W., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140-151.
Ma, Y., Xie, Z., Li, G., Ma, K., Huang, Z., Qiu, Q. J., & Liu, H. (2022). Text visualization for geological hazard documents via text mining and natural language processing. Earth Science Informatics, 15(1), 439-454.
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2-3), 93-118
Mangla, M. Sayyad, A. and Mohanty S. N.(2021) An AI and Computer Vision-based Face Mask Recognition & Detection System.International Conference on Secure Cyber Computing and Communications,pp. 170-174.
Manjari, K. U. Rousha, S. Sumanth, D. and Sirisha Devi, J. (2020). Extractive Text Summarization from Web pages using Selenium and TF-IDF algorithm. International Conference on Trends in Electronics and Informatics,5(2). 648-652.
Martin, W. C., & Lueg, J. E. (2013). Modeling word-of-mouth usage. Journal of Business Research, 66(7), 801-808.
Mohammadhassanzadeh, H., & Shahriari, H. R. (2013). Prediction of user's trustworthiness in web-based social networks via text mining. The ISC International Journal of Information Security, 5(2), 171-187.
Moran, G., & Muzellec, L. (2017). eWOM credibility on social networking sites: A framework. Journal of Marketing Communications, 23(2), 149-161.
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.
Kowsari, K., Meimandi, K. J., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019). Text classification algorithms: a survey. Information, 10(4), 68, Article 150.
Nallapati, R., & Cohen, W. (2021). Link-PLSA-LDA: A New Unsupervised Model for Topics and Influence of Blogs. Proceedings of the International AAAI Conference on Web and Social Media, 2(1), 84-92.
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: market-structure surveillance through text mining. Marketing Science, 31(3), 521-543.
Oded Netzer, Ronen Feldman, Jacob Goldenberg, Moshe Fresko, (2012) Mine Your Own Business: Market-Structure Surveillance Through Text Mining. Marketing Science 31(3):521-543.
Qorich, M., & El Ouazzani, R. (2023). Text sentiment classification of Amazon reviews using word embeddings and convolutional neural networks. Journal of Supercomputing, 79(10), 11029-11054.
Sen, S., & Lerman, D. (2007). Why are you telling me this? An examination into negative consumer reviews on the Web. Journal of Interactive Marketing, 21(4), 76-94.
Teng, Y. W. Day, M. -Y. and Chiu, P. -T.(2022). Text Mining with Information Extraction for Chinese Financial Knowledge Graph. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining,10-1109,421-426.
Trappey, A. J., Chang, A., Trappey, C. V., & Chien, J. Y. (2022). Intelligent RFQ Summarization Using Natural Language Processing, Text Mining, and Machine Learning Techniques. Journal of Global Information Management (JGIM), 30(1), 1-26.
Trusov, M., Bucklin, R. E., & Pauwels, K. (2009). Effects of Word-of-Mouth versus Traditional Marketing: Findings from an Internet Social Networking Site. Journal of Marketing, 73(5), 90-102.
Ul Haq, M. I., Li, Q. M., & Hassan, S. (2019). Text Mining Techniques to Capture Facts for Cloud Computing Adoption and Big Data Processing. Ieee Access, 7, 162254-162267.
Verma, D., & Dewani, P. P. (2021). eWOM credibility: a comprehensive framework and literature review. Online Information Review, 45(3), 481-500.
Wardy, D, Putra, I, Rusjayanthi, D., (2022). Clustering Artikel pada Portal Berita Online Menggunakan Metode K-Means,8(77),65-83.
Yan, C. X., Chang, X. J., Luo, M. N., Zheng, Q. H., Zhang, X. Q., Li, Z. H., & Nie, F. P. (2021). Self-weighted Robust LDA for Multiclass Classification with Edge Classes. Acm Transactions on Intelligent Systems and Technology, 12(1),129-145.
Yang, B. X., Chen, P., Li, X. Y., Yang, F., Huang, Z., Fu, G., Luo, D., Wang, X. Q., Li, W., Wen, L., Zhu, J., & Liu, Q. (2022). Characteristics of High Suicide Risk Messages From Users of a Social Network-Sina Weibo "Tree Hole". Frontiers in psychiatry, 13, 789504.
Yang, J. and Mai, E.S. (2010). Experiential goods with network externalities effects: an empirical study of online rating system. Journal of Business Research,63,9(10), 1050-1057.
Yeh, W. C., Hsieh, Y. L., Chang, Y. C., Hsu & W. L. (2022). Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora. The Association for Computational Linguistics and Chinese Language Processing,2(41),193–199.
Zaman, U., Raza, S. H., Abbasi, S., Aktan, M., & Farías, P. (2021). Sustainable or a Butterfly Effect in Global Tourism? Nexus of Pandemic Fatigue, COVID-19-Branded Destination Safety, Travel Stimulus Incentives, and Post-Pandemic Revenge Travel. Sustainability, 13(22), 213-230.
Zhao, S. Q. (2021). Thumb up or down? A text-mining approach of understanding consumers through reviews. Decision Sciences, 52(3), 699-719.
Zhang, L., Lingreen, A., Ma, B., & Cartwright, D. K. (2013). The impact of online user reviews on cameras sales. European Journal of Marketing, 47(7), 1115-1128.
Zhang, Y., Jin, R., & Zhou, Z.-H. (2010). Understanding bag-of-words model: a statistical framework. International Journal of Machine Learning and Cybernetics, 1(1-4), 43-52.
Zhu, F., & Zhang, X. Q. (2010). Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133-148.