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論文名稱(外文):On Technology of eWOM-based Chance Discovery for Product Innovation
指導教授(外文):Yuh-Min ChenYuh-Jen Chen
外文關鍵詞:Chance DiscoveryKeyGraphsemantic networkword association
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本研究以KeyGraph概念混和語意網路(semantic network)為基礎,發展一「商品創新機會探索」方法,以提供企業了解目前網路上之熱門話題以及從這些討論中來獲取商品創新的機會。本方法將收集到網路口碑進行結構化建立出事件Semantic-KeyGraph,再以密度為基的聚類分析(DBSCAN)分析出熱門的事件,透過擴散性思考及詞彙聯想的原則,進行事件與商品間的商品創新機會探索。此方法可提供企業進行商品創新開發可能的機會,亦可提供企業快速的掌握市場脈動以及提快速了解自身商品在於目前市場中所處的狀態。

Good enterprise product innovation and improvement, are an important factor in business growth. In the past, enterprise through expert interviews, questionnaires and consumers interact to understand consumer demand thereby commodity innovation. But with the rapid development of Internet technology, many consumers in the online publication commodity-related comments and discussion, so that enterprises have more diversified pipeline of understanding of consumer needs, but a large number of network information is difficult to be quickly summarized , result in the inability to make quick and correct decisions for the market changes rapidly changing.
This research attempts to apply KeyGraph concept blending semantic network, development of an eWOM-based Chance Discovery for Product Innovation mechanism, providing enterprise understand those hot discussions on the web and find chance of product innovation from these discussions.
Through KeyGraph plus concept of semantic network, the collected ewom establish a structured event Semantic-KeyGraph, cluster analysis (DBSCAN) density-based analysis of the popular event.Chance discovery for product innovation between popular event and product through the principle of divergent thinking and word association. This mechanism provides enterprise product innovation potential chance, can also provide enterprises to quickly grasp the pulse of the market and know their product’s state of the market is in.

摘要 i
誌謝 vi
目錄 vii
圖目錄 ix
表目錄 xii
第一章、緒論 1
1.1研究背景 1
1.2研究動機 2
1.3研究目的 3
1.4研究問題分析 3
1.5研究項目與方法 4
1.6研究發展程序 6
1.7 論文架構 7
第二章、相關文獻與技術探討 8
2.1 研究領域探討 8
2.2 研究技術探討 12
第三章 網路口碑之商品創新機會探索方法設計 18
3.1 商品創新機會探索模式 18
3.2 網路口碑之商品創新機會探索程序 20
3.2.1網路口碑預處理模式設計(eWOM Preprocessing) 21
3.2.2熱門事件分析模式設計(Popular-Event Analysis) 21
3.2.3商品模型分析模式設計(Product-Model Analysis) 23
3.2.4商品機會探索模式設計(Product Innovation Chance Discovery) 24
第四章、網路口碑之商品創新機會探索技術發展 25
4.1網路口碑預處理 (eWOM Preprocessing) 25
4.2熱門事件分析 (Popular Event Analysis) 30
4.3商品模型分析 (Product-Model Analysis) 43
4.4商品機會探索 (Product Innovation Chance Discovery) 51
第五章、系統實作與驗證 63
5.1 系統架構 63
5.2 實作環境介紹 65
5.3 實作結果 66
5.4 實作實例討論 74
5.4.1事件模型與商品模型所呈現之資訊討論 74
5.4.2事件模型與商品模型間機會探索討論 93
5.4.3字詞關聯討論 98
5.4.4 商品創新機會討論 100
5.5 系統畫面 114
第六章、結論與未來方向 118
6.1 總結 118
6.2 研究限制 119
6.3 未來研究方向 119
參考文獻 121
附錄1:熱門事件模型與商品模型關聯字詞列表 126

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