YouTube影片之情緒分類

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姓名

葉晉昇(Yeh Chin-Sheng) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

YouTube影片之情緒分類

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摘要(中)

隨著web2.0的興起以及網路頻寬不斷的提升與影音串流技術的進步，許多影音網站也隨之出現。目前排行第一名的影音分享網站為YouTube，它的影片上傳總數已經超過十億部影片。如此龐大的影片資料中，為了讓使用者可以快速搜尋到想要的影片，或是尋找有興趣的影片類別，因此，如何管理與分類這些巨量的影音內容成為主要的任務。
YouTube網站上除了上傳影片資料外，還包含其他的使用者產生的內容(User-generated content)，如影片描述、關鍵字、影片標題、影片評論等等。而這些影片上傳者所產生的內容通常帶有主觀意見，並且融入了許多個人化的情緒和想法。YouTube在影片分類方法中並沒有考量情緒的因素，因此本文利用情緒分類將網路影片分類到指定的情緒類別中。
本文首先利用了影音網站的使用者產生的內容(User-generated content)來建立監督式的情緒分類方法，接著利用過去文字為基礎(text-base)的非監督式情緒分類方法來分類影片的情緒，最後我們提出了一個以影音網站的內容為基礎，結合過去情緒分類中的監督式與非監督式兩種方法將影音網站的影片有效的去分類到適合的情緒中。

摘要(英)

With the rise of web 2.0, Video Sharing Websites have become one of the several main sites where people spend most of their time. YouTube is one of the most popular Video Sharing Website and becomes the top most-trafficked website in the world. The numbers of video in YouTube have exceeded one billion. In order to allow user find the video which they are interested in. To manage and classify of these huge amounts of video in YouTube become a major task.
In addition to the video which is uploaded by users, YouTube had many User-generated content. Such as description, keyword, title, comment of these video. The User-generated content in YouTube usually with subjective thoughts and containing personal sentiments and opinions. However the factor of sentiment was not the consideration in the study of YouTube classification in the past, so we focus on sentiment classification in YouTube.
In this study, we collect YouTube data and build an ensemble model to classify the video in YouTube. We based on title, description, keyword, comment features. The ensemble model integrates results from the Supervised learning method and Unsupervised learning method.
Our experiments are executed by the data mining tool and performed by a 10-fold cross-validation. Experiment results show that the performance of our ensemble model outperforms each individual classifier and the features we propose can effectively classify the video in YouTube to correct sentiment.

關鍵字(中)

★ 文字探勘
★ 機器學習
★ 情緒分析
★ YouTube

關鍵字(英)

★ Text mining
★ Machine learning
★ YouTube
★ Sentiment analysis

論文目次

目錄
一、緒論 1
二、文獻探討 6
2.1 網路影片分類 6
2.2 情緒分析 8
2.2.1非監督式學習法 9
2.2.2監督式學習法 10
三、研究方法 11
3.1 研究架構 11
3.2 機器學習法 12
3.2.1資料前處理 12
3.2.2建構特徵向量 13
3.2.3 機器學習法流程 16
3.2情緒字典法: 17
3.2.1資料前處理: 17
3.2.2 建構情緒向量 18
3.3 Ensemble model 19
3.3.1 Ensemble model 1 20
3.3.2 Ensemble model 2 20
四、實驗 21
4.1 實驗設計 21
4.2 實驗結果 23
4.2.1 情緒字典法 23
4.2.2 機器學習法 23
4.2.3 Ensemble model 24
五、結論 27
參考文獻 29

參考文獻

參考文獻
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指導教授

陳彥良(Yen-Liang Chen)

審核日期

2015-7-7

推文