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研究生:郭冠呈
研究生(外文):Kuan-Cheng Kuo
論文名稱:歌詞情緒標註及辨識
論文名稱(外文):Lyrics Sentimental Annotation and Detection
指導教授:林守德林守德引用關係
口試委員:葉彌妍李政德古倫維
口試日期:2015-07-16
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:24
中文關鍵詞:標記人類計算歌詞半監督式學習情緒分類
外文關鍵詞:AnnotationHuman computationLyricsSemi-supervised learningEmotion classification
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  • 收藏至我的研究室書目清單書目收藏:1
近年來,找出音樂的特徵及建立音樂與情緒間的關聯性,並判斷音樂所表達的情緒成了很重要的議題。本論文致力於收集歌詞的情緒標籤並且建立分類模型來偵測歌詞中的情緒。為了收集歌詞的情緒標籤,我們製作了兩個人類計算( Human Computation) 的遊戲並結合了Facebook的社群功能。在第一個遊戲中,我們讓玩家回答一些標記問題,並且在玩家作答時收集情緒標籤。在第二個遊戲中,我們透過收集複數個標籤來驗證標籤的正確性。在收集完標籤後,我們利用歌詞中語意及文法的特徵並使用半監督式學習來訓練分類模型。相較於音樂訊號,歌詞較缺乏旋律的資訊,因此我們也提出一些新的特徵來從歌詞中取得節奏的資訊。

This thesis aims at collecting lyrics sentiment labels for creating a classification model to detect the emotion of songs. To collect the labels of the lyrics, we propose two human computation games. In the first game, the emotion labels are collected when players answer some annotation questions about Chinese lyric. In the second game, multiple labels of a song are collected for verification purpose. The games are implemented with Facebook API. The second goal of this thesis is to construct a model for lyric sentiment detection. We propose several syntactic and semantic features and exploit the semi-supervised learning approaches to learn the classification model. Furthermore, some heuristics are proposed to extract information about the tempo of songs from text to compensate for the insufficient melodic information in lyrics.

中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES v
LIST OF TABLES vi
Chapter 1 Introduction 1
Chapter 2 Related Work 4
2.1 Emotion model 4
2.2 Human computation application 5
2.3 Lyrics approach for emotion classification 7
Chapter 3 Human computation games: LyricsMatcher and Song-request 9
3.1 Game Mechanisms: LyricsMatcher 9
3.2 Game Mechanisms: Song-request 12
3.3 Song preprocessing and partition 13
Chapter 4 Lyrics emotion classification 15
4.1 lyrics features 15
4.2 semi-supervised learning 17
Chapter 5 Experiment 18
5.1 lyrics features 18
5.2 semi-supervised learning and heuristic features 19
Chapter 6 Conclusion 21
REFERENCE 22



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