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題名 以推敲可能性模式探討影響評論幫助性之因素
Factors Affecting Review Helpfulness : An Elaboration Likelihood Model Perspective
作者 熊耿得
Hsiung, Keng-Te
貢獻者 梁定澎<br>莊皓鈞
Liang, Ting-Peng<br>Chuang, Howard Hao-Chun
熊耿得
Hsiung, Keng-Te
關鍵詞 評論幫助性
推敲可能性模式
LDA主題模型
環狀情緒模型
情感分析
Review helpfulness
Elaboration likelihood model
Latent dirichlet allocation
Circumplex model
Sentiment analysis
日期 2017
上傳時間 28-Sep-2017 10:42:00 (UTC+8)
摘要 在電子商務中，評論會影響消費者的購買決策，透過評論幫助性可以篩選出關鍵的評論，以利消費者進行決策。本研究以推敲可能性模式作為研究架構，透過文字探勘挖掘評論的文本特性來探討影響幫助性之要素，中央線索除了評論長度與可讀性外，利用LDA主題模型衡量評論主題廣度；周邊線索則是透過環狀情緒模型進行情感分析，並透過評論者排名來衡量來源可信度，利用亞馬遜商店中的資料進行驗證分析。結果發現，消費者在判斷評論幫助性時，會參考中央以及周邊線索。具備高論點品質的中央線索將有效提升評論幫助性；周邊線索整體而言，證實了社會中存在負向偏誤，具備喚起度的負向情感較容易提升評論幫助性，而評論是否被認為有幫助確實會受到評論者的排名所影響。進階分析結果顯示，周邊的情感效果會受到評論者排名高低的影響，前段評論者應保持中立避免帶有個人情緒；中段評論者的評論幫助性會隨著情緒喚起度而增加；後段評論者則需要增加自身的負向情感，才能夠對於評論幫助性有正向影響。
Online reviews are important factors in consumers’ purchase decision. The helpfulness of reviews allows consumers to quickly identify useful reviews. The purpose of this study is to investigate the nature of online reviews that affect their helpfulness through the lens of the elaboration likelihood model. For the central cues, we adopt latent dirichlet allocation to measure review breadth in addition to review length and review readability. For the peripheral cues, we use the sentiment analysis based on the circumplex model to catch the emotion effect and use the ranking of the reviewers to measure the source credibility. We used a dataset collected from Amazon.com to evaluate our model. The result suggests that consumers focus both central and peripheral cues when they read reviews. Consumers care about the length, breadth and readability of reviews associated with the central route, and the emotional effects associated with the peripheral route. In the advanced research, we split our sample into 3 groups by their ranking of the reviewers. We found that the top reviewers should keep neutral and avoid personal feelings to make their reviews more helpful; the middle reviewers can use more arousal words to improve their review helpfulness; the bottom reviewers must increase their emotional valence strength, especially the negative emotion to higher the perceived review helpfulness.
參考文獻 王韋堯, 黃詩珮, & 劉怡寧. (2012). 消費品廣告設計之情緒效價與喚起分析. 設計學報 (Journal of Design), 17(3).
陳怡安. (2008). 口碑基本概論: 以口碑領域文獻為依據.
黃俊堯, & 柳秉佑. (2016). 消費者線上口碑與評論研究:國內外相關文獻回顧與討論. 臺大管理論叢, 26(3), 215 - 256.
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research, 4(3), 291-295.
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.
Bellezza, F. S., Greenwald, A. G., & Banaji, M. R. (1986). Words high and low in pleasantness as rated by male and female college students. Behavior Research Methods, Instruments, & Computers, 18(3), 299-303.
Berger, J. (2011). Arousal Increases Social Transmission of Information. Psychological Science, 22(7), 891-893.
Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205.
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. Journal of machine Learning research, 3(Jan), 993-1022.
Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Retrieved from
Brysbaert, M., New, B., & Keuleers, E. (2012). Adding part-of-speech information to the SUBTLEX-US word frequencies. Behavior research methods, 44(4), 991-997.
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.
Chang, J., Boyd-Graber, J. L., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Paper presented at the Nips.
Chen, X., Sheng, J., Wang, X., & Deng, J. (2016). Exploring Determinants of Attraction and Helpfulness of Online Product Review: A Consumer Behaviour Perspective. Discrete Dynamics in Nature and Society, 2016(1), 1-19.
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.
Chung, H. C., Lee, H., Koo, C., & Chung, N. (2017). Which Is More Important in Online Review Usefulness, Heuristic or Systematic Cue? Information and Communication Technologies in Tourism 2017 (pp. 581-594): Springer.
Cox, D. F. (1967). Risk taking and information handling in consumer behavior.
Day, G. S. (1971). Attitude change, media and word of mouth. Journal of Advertising Research.
Dong, R., Schaal, M., O’Mahony, M. P., McCarthy, K., & Smyth, B. (2012). Harnessing the Experience Web to Support User-Generated Product Reviews. Paper presented at the 20th International Conference on Case-Based Reasoning, Lyon, France.
eMarketer. (2016). Worldwide Retail Ecommerce Sales Will Reach $1.915 Trillion This Year. Retrieved from https://www.emarketer.com/Article/Worldwide-Retail-Ecommerce-Sales-Will-Reach-1915-trillion-This-Year/1014369
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Flesch, R. (1948). A new readability yardstick. Journal of applied psychology, 32(3), 221.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313.
Fox, E. (2008). Emotion science cognitive and neuroscientific approaches to understanding human emotions: Palgrave Macmillan.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235.
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.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. The Journal of Marketing, 50-68.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
Hu, N., Liu, L., & Zhang, J. J. (2008). Do online reviews affect product sales? The role of reviewer characteristics and temporal effects. Information Technology and Management, 9(3), 201-214.
Hwang, S.-Y., Lai, C.-Y., Chang, S., & Jiang, J.-J. (2015). The identification of noteworthy hotel reviews for hotel management. Pacific Asia Journal of the Association for Information Systems, 6(5).
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.
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Kuperman, V., Stadthagen-Gonzalez, H., & Brysbaert, M. (2012). Age-of-acquisition ratings for 30,000 English words. Behavior research methods, 44(4), 978-990.
Laroche, M., Babin, B. J., Lee, Y.-K., Kim, E.-J., & Griffin, M. (2005). Modeling consumer satisfaction and word-of-mouth: restaurant patronage in Korea. Journal of Services Marketing, 19(3), 133-139.
Latent Dirichlet allocation - Wikipedia. Retrieved from https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
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Liang, T.-P., Li, X., Yang, C.-T., & Wang, M. (2015). What in Consumer Reviews Affects the Sales of Mobile Apps: A Multifacet Sentiment Analysis Approach. International Journal of Electronic Commerce, 20(2), 236-260.
Luan, P., Zhang, N., & Han, Y. (2014). The Study on Influences of Online Review Helpfulness. Paper presented at the The Pacific Asia Conference on Information Systems, Chengdu, China.
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Mousavizadeh, M., Koohikamali, M., & Salehan, M. (2015). The Effect of Central and Peripheral Cues on Online Review Helpfulness: A Comparison between Functional and Expressive Products. ICIS.
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描述 碩士
國立政治大學
資訊管理學系
105356015
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105356015
資料類型 thesis
dc.contributor.advisor 梁定澎<br>莊皓鈞zh_TW
dc.contributor.advisor Liang, Ting-Peng<br>Chuang, Howard Hao-Chunen_US
dc.contributor.author (Authors) 熊耿得zh_TW
dc.contributor.author (Authors) Hsiung, Keng-Teen_US
dc.creator (作者) 熊耿得zh_TW
dc.creator (作者) Hsiung, Keng-Teen_US
dc.date (日期) 2017en_US
dc.date.accessioned 28-Sep-2017 10:42:00 (UTC+8)-
dc.date.available 28-Sep-2017 10:42:00 (UTC+8)-
dc.date.issued (上傳時間) 28-Sep-2017 10:42:00 (UTC+8)-
dc.identifier (Other Identifiers) G0105356015en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/113128-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 105356015zh_TW
dc.description.abstract (摘要) 在電子商務中，評論會影響消費者的購買決策，透過評論幫助性可以篩選出關鍵的評論，以利消費者進行決策。本研究以推敲可能性模式作為研究架構，透過文字探勘挖掘評論的文本特性來探討影響幫助性之要素，中央線索除了評論長度與可讀性外，利用LDA主題模型衡量評論主題廣度；周邊線索則是透過環狀情緒模型進行情感分析，並透過評論者排名來衡量來源可信度，利用亞馬遜商店中的資料進行驗證分析。結果發現，消費者在判斷評論幫助性時，會參考中央以及周邊線索。具備高論點品質的中央線索將有效提升評論幫助性；周邊線索整體而言，證實了社會中存在負向偏誤，具備喚起度的負向情感較容易提升評論幫助性，而評論是否被認為有幫助確實會受到評論者的排名所影響。進階分析結果顯示，周邊的情感效果會受到評論者排名高低的影響，前段評論者應保持中立避免帶有個人情緒；中段評論者的評論幫助性會隨著情緒喚起度而增加；後段評論者則需要增加自身的負向情感，才能夠對於評論幫助性有正向影響。zh_TW
dc.description.abstract (摘要) Online reviews are important factors in consumers’ purchase decision. The helpfulness of reviews allows consumers to quickly identify useful reviews. The purpose of this study is to investigate the nature of online reviews that affect their helpfulness through the lens of the elaboration likelihood model. For the central cues, we adopt latent dirichlet allocation to measure review breadth in addition to review length and review readability. For the peripheral cues, we use the sentiment analysis based on the circumplex model to catch the emotion effect and use the ranking of the reviewers to measure the source credibility. We used a dataset collected from Amazon.com to evaluate our model. The result suggests that consumers focus both central and peripheral cues when they read reviews. Consumers care about the length, breadth and readability of reviews associated with the central route, and the emotional effects associated with the peripheral route. In the advanced research, we split our sample into 3 groups by their ranking of the reviewers. We found that the top reviewers should keep neutral and avoid personal feelings to make their reviews more helpful; the middle reviewers can use more arousal words to improve their review helpfulness; the bottom reviewers must increase their emotional valence strength, especially the negative emotion to higher the perceived review helpfulness.en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 5
第一節 網路口碑的發展 5
第二節 評論幫助性 7
第三節 推敲可能性模式 10
第四節 LDA主題模型 14
第五節 情感分析 16
第三章 研究架構與假說 21
第一節 研究架構 21
第二節 研究假說 22
第四章 研究方法與資料 25
第一節 次級資料分析 25
第二節 資料來源與變數說明 25
第三節 資料處理 26
一、 前處理 26
二、 變數處理 28
三、 變數範例 32
第四節 分析模型 33
第五章 研究結果 35
第一節 分析結果 35
一、 評論之中央線索 36
二、 評論之周邊線索 36
第二節 進階探討及分析 38
一、 不同來源可信度分析 38
二、 中央與周邊線索比較 42
第六章 結論 44
第一節 研究發現 44
第二節 研究貢獻 46
一、 學術貢獻 46
二、 實務貢獻 46
第三節 研究限制與未來建議 47
參考文獻 48		zh_TW
dc.format.extent 4015130 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105356015en_US
dc.subject (關鍵詞) 評論幫助性zh_TW
dc.subject (關鍵詞) 推敲可能性模式zh_TW
dc.subject (關鍵詞) LDA主題模型zh_TW
dc.subject (關鍵詞) 環狀情緒模型zh_TW
dc.subject (關鍵詞) 情感分析zh_TW
dc.subject (關鍵詞) Review helpfulnessen_US
dc.subject (關鍵詞) Elaboration likelihood modelen_US
dc.subject (關鍵詞) Latent dirichlet allocationen_US
dc.subject (關鍵詞) Circumplex modelen_US
dc.subject (關鍵詞) Sentiment analysisen_US
dc.title (題名) 以推敲可能性模式探討影響評論幫助性之因素zh_TW
dc.title (題名) Factors Affecting Review Helpfulness : An Elaboration Likelihood Model Perspectiveen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 王韋堯, 黃詩珮, & 劉怡寧. (2012). 消費品廣告設計之情緒效價與喚起分析. 設計學報 (Journal of Design), 17(3).
陳怡安. (2008). 口碑基本概論: 以口碑領域文獻為依據.
黃俊堯, & 柳秉佑. (2016). 消費者線上口碑與評論研究:國內外相關文獻回顧與討論. 臺大管理論叢, 26(3), 215 - 256.
Arndt, J. (1967). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research, 4(3), 291-295.
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.
Bellezza, F. S., Greenwald, A. G., & Banaji, M. R. (1986). Words high and low in pleasantness as rated by male and female college students. Behavior Research Methods, Instruments, & Computers, 18(3), 299-303.
Berger, J. (2011). Arousal Increases Social Transmission of Information. Psychological Science, 22(7), 891-893.
Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205.
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. Journal of machine Learning research, 3(Jan), 993-1022.
Bradley, M. M., & Lang, P. J. (1999). Affective norms for English words (ANEW): Instruction manual and affective ratings. Retrieved from
Brysbaert, M., New, B., & Keuleers, E. (2012). Adding part-of-speech information to the SUBTLEX-US word frequencies. Behavior research methods, 44(4), 991-997.
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.
Chang, J., Boyd-Graber, J. L., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. Paper presented at the Nips.
Chen, X., Sheng, J., Wang, X., & Deng, J. (2016). Exploring Determinants of Attraction and Helpfulness of Online Product Review: A Consumer Behaviour Perspective. Discrete Dynamics in Nature and Society, 2016(1), 1-19.
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.
Chung, H. C., Lee, H., Koo, C., & Chung, N. (2017). Which Is More Important in Online Review Usefulness, Heuristic or Systematic Cue? Information and Communication Technologies in Tourism 2017 (pp. 581-594): Springer.
Cox, D. F. (1967). Risk taking and information handling in consumer behavior.
Day, G. S. (1971). Attitude change, media and word of mouth. Journal of Advertising Research.
Dong, R., Schaal, M., O’Mahony, M. P., McCarthy, K., & Smyth, B. (2012). Harnessing the Experience Web to Support User-Generated Product Reviews. Paper presented at the 20th International Conference on Case-Based Reasoning, Lyon, France.
eMarketer. (2016). Worldwide Retail Ecommerce Sales Will Reach $1.915 Trillion This Year. Retrieved from https://www.emarketer.com/Article/Worldwide-Retail-Ecommerce-Sales-Will-Reach-1915-trillion-This-Year/1014369
Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89.
Flesch, R. (1948). A new readability yardstick. Journal of applied psychology, 32(3), 221.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291-313.
Fox, E. (2008). Emotion science cognitive and neuroscientific approaches to understanding human emotions: Palgrave Macmillan.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235.
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.
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