Lyrics Sentimental Annotation and Detection
Date Issued
2015
Date
2015
Author(s)
Kuo, Kuan-Cheng
Abstract
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.
Subjects
Annotation
Human computation
Lyrics
Semi-supervised learning
Emotion classification
Type
thesis
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