臺灣博碩士論文加值系統

為了瞭解是否能藉由微電影來為品牌做行銷，提高大眾對品牌的認識程度以及討論度，本研究分為兩個階段，第一階段利用爬蟲抓取在網路論壇中的討論文章、留言等數據，透過自然語言處理中Jieba分詞工具將留言及文章進行分詞，接著以機器學習中的支持向量機器(Vector Support Machine)及隨機森林(Random Forest)進行網路文章及留言的分析，最後針對幾項主要網路討論議題進行探討；第二階段針對品牌對外的宣傳媒介進行貼文的分析並針對分析結果提供建議。研究結果發現研究目標在網路上的討論聲量並不理想、知名度不高而在第二階段所得到的結果顯示人們在觀看貼文時，比起滿是文字的貼文，更覺得有影片的貼文更吸睛，提高了續看率，因此我們提出了透過拍攝微電影來比較品牌宣傳前後的網路聲量。研究結果表示利用微電影來宣傳是可以提高網路聲量，甚至提高了報名人數。在本篇研究中我們了解隨著媒體傳播型式的改變，消費者的購買模式也跟著改變，而消費者對廣告類型的接受程度也考驗著商人的行銷方式。

In order to understand whether it is possible to market for brands through micro-movies and to raise public awareness of the brand and discussion, the research is divided into two phases. The first phase uses crawlers to crawl discussion articles and messages in online forums. And other data, through the natural language processing in the Jieba word segmentation tool to segment the message and article, then use the support vector machine and random forest in machine learning to analyze the online articles and messages, and finally discuss several major online discussion topics. The second stage analyzes the post for the brand's external media and provides suggestions for the analysis results. The results of the study found that the research target's discussion volume on the Internet was not ideal, and the popularity was not high. The results obtained in the second stage showed that people were more likely to have a film when they viewed the post than the text-filled post. The post is more eye-catching and improves the renewal rate, so we proposed to compare the network volume before and after the brand promotion by shooting micro-movies. The results of the study indicate that the use of micro-movies to promote can increase the volume of the Internet and even increase the number of applicants. In this study, we understand that as the type of media communication changes, the consumer's buying pattern also changes, and the consumer's acceptance of the type of advertising also tests the merchant's marketing style.

摘要 I
致謝 II
圖目錄 VI
表目錄 X
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 4
第二章 文獻探討 5
2.1 六度分離理論(Six Degree of Separation) 5
2.2 Facebook商業行為 5
2.3 廣告的生動性(Vividness Effect) 6
2.4 微電影廣告的特徵 7
2.5 自然語言處理(Natural Language Processing) 8
2.6 機器學習(Machine Learning) 9
2.7 文字探勘(Text Mining) 10
第三章 研究方法 12
3.1 研究流程 12
3.2 資料蒐集 13
3.3 建立計算模型 17
3.4 假設檢定 18
3.5 文字探勘 20
3.6 自然語言處理 24
3.6.1 Jieba分詞 24
3.6.2 TF-IDF 26
3.7 機器學習 28
3.7.1 支持向量機(Support Vector Machine) 28
3.7.2 隨機森林(Random Forest) 31
3.8 微電影賽程設計 32
第四章 資料分析 34
4.1 網路論壇討論度分析 35
4.1.1 抓取數據 35
4.1.2 Jieba分詞 37
4.1.3 TF-IDF 38
4.1.4 支持向量機(SVM)及隨機森林(Random Forest) 38
4.1.5 論壇討論度分析結論 42
4.2 Facebook相關功能 46
4.3 洞察報告數據分析 57
4.3.1 數據集介紹 57
4.3.2 正常化數據（Normalization） 57
4.3.3 常態檢定 60
4.3.4 成對T檢定（Pair T-test） 62
4.3.5 無母數單因子分析（Nonparametric Statistics） 63
4.3.6 洞察報告分析結論 65
4.4 小結 65
4.5 微電影比賽後的討論 66
4.5.1 網路聲量面相比較 68
4.5.2 微電影賽後的影響 70
4.5.3 微電影賽後分析結論 75
第五章 結論 77
參考文獻 79
附錄 86
常態檢定 86
T檢定 87
無母數分析 88
線性迴歸 89

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