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.

由於網際網路的普及造成資訊量愈來愈大，在資訊的搜尋、整理與閱讀上會耗費許多時間，因此本研究提出一應用文本主題及關係探勘的方法，將多份文件自動生成一篇摘要，以幫助使用者能降低資訊的閱讀時間，並能快速理解文件所欲表達之意涵。 本研究以電影評論文章為例，結合文章結構的概念，將影評摘要分為「電影資訊」、「電影劇情介紹」及「心得結論」三部分，其中「電影資訊」及「心得結論」為透過本研究建置之電影領域相關詞庫比對得出。接著將餘下之段落歸屬於「電影劇情介紹」，並透過LDA主題模型將段落分群，再運用主題關係地圖的概念挑選各群之代表段落並排序，最後將各段落去除連接詞及將代名詞還原為其所指之主詞，以形成一篇列點式影評摘要。 研究結果顯示，本研究所實驗之三部電影，產生之摘要能涵蓋較多的資訊內容，提升了摘要之多樣性，在與最佳範本摘要的相似度比對上，分別提升了10.8228%、14.0123%及25.8142%，可知本研究方法能有效掌握文件之重點內容，生成之摘要更為全面，藉由此方法讓使用者自動彙整電影評論文章，以生成一精簡之摘要，幫助使用者節省其在資訊的搜尋及閱讀的時間，以便能快速了解相關電影之資訊及評論。 / The rapid development of information technology over the past decades has dramatically increased the amount of online information. Because of the time-wasting on absorbing large amounts of information for users, we would like to present a method in this thesis by using text topic and relationship mining for multi-document summarization to help users grasp the theme of multiple documents quickly and easily by reading the accurate summary without reading the whole documents. We use movie reviews as an example of multi-document summarization and apply the concept of article structures to categorize summary into film data, film orientation and conclusion by comparing the thesaurus of movie review field built by this thesis. Then we cluster the paragraphs in the structure of film orientation into different topics by Latent Dirichlet Allocation (LDA). Next, we apply the concept of text relationship map, a network of paragraphs and the node in the network referring to a paragraph and an edge indicating that the corresponding paragraphs are related to each other, to extract the most important paragraph in each topic and order them. Finally, we remove conjunctions and replace pronouns with the name it indicates in each extracted paragraph s and generate a bullet-point summary. From the result, the summary produced by this thesis can cover different topics of contents and improve the diversity of the summary. The similarities compared with the produced summaries and the best-sample summaries raise of 10.8228%, 14.0123% and 25.8142% respectively. The method presented in this thesis grasps the key contents effectively and generates a comprehensive summary. By providing this method, we try to let users aggregate the movie reviews automatically and generate a simplified summary to help them reduce the time in searching and reading articles.