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論文中文名稱:社群粉絲團情感辨識之研究 [以論文名稱查詢館藏系統]
論文英文名稱:Sentiment Recognition Research on Social Community by Using Fan Pages [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與財金管理系碩士班
畢業學年度:104
畢業學期:第二學期
中文姓名:張皓程
英文姓名:Chang, Hao-Cheng
研究生學號:103AB8003
學位類別:碩士
語文別:中文
口試日期:2016/07/05
指導教授中文名:翁頌舜
指導教授英文名:Weng, Sung-Shun
口試委員中文名:翁頌舜;王貞淑;陳仲儼;楊亨利
中文關鍵詞:社群行銷網路廣告文字探勘情感分析
英文關鍵詞:Social Media MarketingInternet AdvertisingText MiningSentiment Analysis
論文中文摘要:在廣告充斥的時代,社群媒體已成為行銷傳播活動的重要管道,個人與企業紛紛透過社交網絡的影響力進而發展可能商機。然而,面對雜亂無章的資訊及過高的成本,對廣告商及使用者來說,都是一種負擔。該如何找出使用者真正所需要的廣告,並投遞精準的廣告資訊給使用者,以達到有效的行銷策略,正是本研究的目的。
本研究藉由文字探勘技術,結合Facebook粉絲頁,蒐集所需資料,利用斷詞系統、TF-IDF、矩陣分解及情感分析進行運算,將留言字詞進行處理,計算出更細微的特徵。結果發現的確能有效找出目標社群使用者,並提供廣告商投遞廣告時的依據。
論文英文摘要:In an era full of advertisements, social media has become a major channel for marketing communications activities. Individuals and companies take the advantage of the social networks to develop more opportunities. However, due to high cost and disorganized information, it has become a burden for advertisers and users. The subject of this study is to find out what kind of advertisements that users are looking for, deliver the precise information to users, and achieve an effective marketing strategy.
This study has used text mining technology and Facebook fan pages to collect the required information. We also used the Jieba hyphenation system, TF-IDF, matrix decomposition and emotional analysis operations to process messages and texts. This allows us to calculate and catch subtle characteristics. The results show that it can effectively identify the specific users and provide the statistics to advertisers, which help them to deliver the advertisements to the right places.
論文目次:摘要 I
ABSTRACT II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 2
1.3研究流程 4
第二章 文獻探討 6
2.1社群網絡 6
2.1.1社群媒體行銷 7
2.1.2Facebook 8
2.2網路廣告 9
2.2.1社群廣告 11
2.2.2客製化廣告 12
2.2.3廣告轉換率 13
2.3網路爬蟲 14
2.4詞頻與逆向文件頻率 16
2.5矩陣分解 17
2.5.1奇異值分解 17
2.6情感分析 19
第三章 研究設計與方法 21
3.1研究架構 21
3.2資料蒐集 22
3.2.1Facebook爬蟲程式 22
3.2.2Facebook Graph API 23
3.2.3Parsing Unit 25
3.2.4欄位格式 26
3.3資料預處理 28
3.3.1斷詞處理 28
3.3.2停止詞過濾 30
3.3.3字詞權重計算 31
3.4特徵擷取 32
3.5情感分析模組 34
3.5.1情感分析特徵詞 35
3.5.2特徵詞語意分類 36
3.5.3情感分析器 37
3.6廣告投放機制 37
第四章 實驗結果與分析 39
4.1實驗環境 39
4.2實驗設計 39
4.3資料來源與處理 40
4.4評估方式 43
4.5實驗結果 46
4.5.1資料篩選 46
4.5.2情感分析判斷 47
4.5.3目標客群發現 50
4.5.4廣告投放方法與驗證 50
第五章 結論 55
5.1研究結論與貢獻 55
5.2研究限制與未來展望 56
參考文獻 58
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