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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/113128
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/113128

    Title: 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
    Authors: 熊耿得
    Hsiung, Keng-Te
    Contributors: 梁定澎

    Liang, Ting-Peng
    Chuang, Howard Hao-Chun

    Hsiung, Keng-Te
    Keywords: 評論幫助性
    Review helpfulness
    Elaboration likelihood model
    Latent dirichlet allocation
    Circumplex model
    Sentiment analysis
    Date: 2017
    Issue Date: 2017-09-28 10:42:00 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105356015
    Data Type: thesis
    Appears in Collections:[資訊管理學系] 學位論文

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