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研究生:蔡伊玲
研究生(外文):Yi-Ling Cai
論文名稱:使用上下文關聯性改善中文意見探勘系統的效能
論文名稱(外文):Improving the Performance of Chinese Opinion-Mining System by Context Dependent
指導教授:蔣璿東
口試委員:葛煥昭王鄭慈
口試日期:2017-05-26
學位類別:碩士
校院名稱:淡江大學
系所名稱:資訊工程學系資訊網路與多媒體碩士班
學門:電算機學門
學類:網路學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:70
中文關鍵詞:中文意見探勘意見探勘中文意見探勘系統口碑分析
外文關鍵詞:Chinese Opinion-MiningOpinion MiningMouth MarketingChinese Opinion-Mining System
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隨著網路技術的快速發展,大部份消費者在購買某項產品或某公司的服務時,會先瀏覽網站上的相關評論之後,再進行購買行為。相關研究指出,評論文章會影響消費者對產品或某公司服務的購買決策。所以對於公司而言,公司利用顧客評論的文章做各面向(aspect)的口碑分析及查看網友們的意見,以便於能在最短的時間內回覆和平衡與公司相關的負面評價,這是件非常重要的工作。因此就台灣的公司必須利用中文意見探勘系統來做各面向的口碑分析,我們已經初步發展了一個屬於aspect-level的中文意見探勘系統;此系統分別利用default topic和 default feature來增加意見和所要討論面向的回收率。此研究我們將利用意見元素間的上下文關聯性讓系統不但能推論出部分網友在回文中所要討論的topic、面向和子面向,同時亦能修正部分由default topic和 default feature所造成的錯誤;此增加的功能是能讓使用者在做口碑分析時,能獲得更詳細和正確的資訊。
With the development of network technology, before people buy a product or a company''s service, most consumers will search the related comments on the social networking sites. Related studies indicate that the product evaluation article will influence the purchase decisions of consumers. Therefore, analyzing WOM (word of mouth) to different aspects and replying negative WOM are important things to companies. Since opinion analysis at document level and sentence level is too coarse to determine users’ opinions precisely, we have developed an aspect-level Chinese opinion mining system for a specific domain. The system uses default topic and default feature to increase recall rate of opinion mining results and aspects, respectively. In this project, we will use context dependent not only to derive some unknown topics, features and sub-features but also to correct some default topic and default feature errors. Consequently, uses can get more detail and correct information from WOM analysis.
目錄
第一章 緒論 1
1-1 研究動機與目的 1
1-2 研究架構 6
第二章 文獻探討 7
2-1 中文意見探勘系統相關研究 7
2-2 本研究室開發的中文意見探勘系統簡介 11
第三章 問題陳述 16
3-1 回文沒有提對象的問題 16
3-2 預設意見元素的問題 18
3-2-1 Default Topic 18
3-2-2 Default Feature 和Default Item 20
第四章 研究方法 24
4-1 意見元素間的上下文關聯性 24
4-2 CDA演算法 28
第五章 實驗結果 32
5-1資料來源 32
5-2 CDA演算法的單月成效分析 36
5-2-1 修正default topic的單月成效 36
5-2-2 修正default feature的單月成效 39
5-2-3 補充面向與子面向資訊的單月成效 41
5-2-4 各面向單月情形 42
5-3 CDA演算法的成效分析 44
5-3-1 資料來源 44
5-3-2 成效分析 45
第六章 結論與未來展望 49
參考文獻 50
附錄A 英文論文 53

圖目錄
圖 1中文意見探勘系統架構圖 12
圖 2 文章下載與配置流程圖 13
圖 3中文意見探勘系統文章分析流程圖 15
圖 4 PTT論壇標題範例(一) 17
圖 5 PTT論壇標題範例(三) 20
圖 6 PTT論壇標題範例(四) 21
圖 7 PTT論壇標題範例(五) 22
圖 8 MOBILE01論壇標題範例(六) 23
圖 9 THE CONTEXT DEPENDENT ALGORITHM 28
圖 10資料來源頻道比例圖 33
圖 11 中華各面向原始評價圖 43
圖 12 經演算法修正後中華各面向評價圖 43

表目錄
表 1意見元素定義表 2
表 2 關鍵字與討論標題對照表 35
表 3 ISP領域2016-01-2016-06月分析資料數量 35
表 4 2016-01月針對DEFAULT TOPIC完整句統計表 38
表 5 DEFAULT TOPIC錯誤完整句統計表 38
表 6 2016-01月針對DEFAULT FEATURE完整句統計表 40
表 7 2016-01月針對增加面向的完整句統計表 41
表 8 2016-01月針對增加子面向的完整句統計表 42
表 9 ISP領域2016-01-2016-06月分析輸出完整句比較表 45
表 10 2016-01~06月DEFAULT TOPIC完整句比較表 46
表 11 2016-01~06月DEFAULT FEATURE完整句統計表 47
表 12 2016-01~06月補充面向資訊統計表 48
表 13 2016-01~06月補充子面向資訊統計表 48
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