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研究生: 鄭彤怡
Cheng, Tung-I
論文名稱: 類神經網路在行銷主軸與產品文案應用
Application of artificial neural network to determine product marketing structure and content
指導教授: 施人英
Shih, Jen-Ying
學位類別: 碩士
Master
系所名稱: 高階經理人企業管理碩士在職專班(EMBA)
Executive Master of Business Administration
論文出版年: 2018
畢業學年度: 106
語文別: 中文
論文頁數: 40
中文關鍵詞: 深度學習自然語言文本生成產品行銷文案
英文關鍵詞: Copywriting generator, Product Marketing
DOI URL: http://doi.org/10.6345/THE.NTNU.EMBA.045.2018.F08
論文種類: 學術論文
相關次數: 點閱:181下載:31
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  • 一個好的行銷文案計劃需要許多專業的角色共同完成,創意的策略及設計通常需要長時間生活的內化, 所以文案沒有對與錯而是能不能勾動閱讀者的感受。
    對於管理者的難題,常常也在於無法量化或有快速有效的方式獲得提案,產品的週期往往也會在反覆的討論與無法判斷選擇提案的可行性中讓產品時程有所滯延。
    2018科技話題圍繞著人工智慧AI (Artificial Intelligence) ,AI提供世界許多解決方案,用AI來生成行銷語言,使用自然語言處理和生成比人力撰稿花費的時間短,也有可快速量化、生成和優化的優勢。
    本研究著重探討商業行為裡行銷活動中的文案是否可利用深度學習與自然語言處理來生成文案之可行性的評估與相關實驗,以期利用該類系統應用於文案生成的結果來有效減少傳統人力作業模式的資源耗損,藉由增加效率來解決管理上的問題。

    Marketing related activities have got more and more importance in the business world nowadays, and to write a decent copywriting gets even more considerable among all kinds of work related to digital marketing. There is no good or bad copywriting, the only thing matters is: Can it resonate the most with target audience? Inspired by sophisticated AI provides all kind of solutions and both of a very trendy AI application, NLP and highly appreciation of creating touching copywriting when running a business, the theme of this thesis is to study whether AI can automatically generate useful copywriting by training a designated RNN model with a large amount of existing copywriting data and then having a business category chosen as a keyword to input.
    This kind of technology could significantly reduce human effort of manually constructing slogans or promoting sentences. In addtion, efficiency brought by such application would help business managers to avoid back and forth discussion which could delay projects and not able to compete in current rapid business environment.

    摘要 I ABSTRACT II 誌謝 III 表目錄 VI 圖目錄 VII 第一章、緒論 1 第一節、研究背景 1 第二節、研究內容與目的 3 第三節、論文組織與架構 4 第二章、文獻探討 5 第一節、產品行銷理論 5 第二節、LANGUAGE MODEL語言模型 6 第三節、DEEP LEARNING深度學習 8 第四節、文本生成 10 第五節、定量分析方法 10 第三章、研究方法 14 第一節、文字建模 14 第二節、模型建構-類神經網路 15 1 RNN 15 2 LSTM網路 17 3優化器 17 4詞向量 18 第三節、研究限制 19 1 資料量 19 2 Overfitting (過度擬合) 19 第四章、資料分析與解釋 24 第一節、文字資料預處理 24 第二節、訓練用RNN模型 24 2.1 Tensorboard訓練可視化圖表 26 第三節、輸出文案資料 27 第四節、文案可行性調查 29 第五章、結論 33 第一節、研究發現 33 第二節、建議 33 參考文獻 36 一、英文文獻: 36 二、中文文獻: 40

    一、 英文文獻:
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    二、中文文獻:
    邱炯友(2003)。學術電子期刊同儕評閱之探析。教育資料與圖書館學,40(3),309-323。
    宋曜廷、陳茹玲、李宜憲、查日龢、曾厚強、林維駿、張道行、張國恩(2013)。中文文本可讀性探討:指標選取、模型建立與效度驗證。中華心理學刊,55(1),75-106。
    陳建宏、蔡筱倩、郭伯臣、廖晨惠、楊裕貿(2013)。電腦自動化文本分析與詞類之探究。EITS2013數位教學暨資訊實務研討會,南台科技大學。
    LYNN (2017)。民107年4月20日 取自: https://hellolynn.hpd.io/2017/11/10/2012%E5%B9%B4%E4%BB%A4%E6%B7%B1%E5%BA%A6%E5%AD%B8%E7%BF%92%E5%92%8Cnvidia%E8%82%A1%E5%83%B9%E7%81%AB%E7%88%86%E8%B5%B7%E4%BE%86%E7%9A%84%E7%9C%9F%E6%AD%A3%E9%97%9C%E9%8D%B5%E2%94%80%E2%94%80gpu/
    張睿(2014) 。民107年4月5日 取自:https://www.cbnweek.com/articles/normal/20800
    張睿(2018) 。民107年5月5日 取自http://finance.sina.com.cn/roll/2018-04-24/doc-ifzqvvsa1387051.shtml
    句子迷(n.d.) 。民107年3月2日 取自 :https://www.youtube.com/watch?v=tleeC-KlsKA

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