The Semantic Shapes of Popular Music Lyrics: Graph-Based Representation, Analysis, and Interpretation of Popular Music Lyrics in Semantic Natural Language Embedding Space

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2018-12-01 DOI:10.1109/ICMLA.2018.00203

M. Ogihara, Daniel Galarraga, Gang Ren, T. Tavares

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Abstract

Popular music lyrics are usually brief in length yet sophisticated in narrative content, emotional expression, and structural aesthetics. In this paper, we propose a graph-based analysis and interpretation framework for popular music lyrics using the sematic word embedding representation. This framework explores the temporal and structural information in music lyrics, such as word sequential pattern, lyric format pattern, and predominate song forms, to enhance the understanding of the interaction between the semantic and structural properties of music lyrics. Our proposed analysis and interpretation framework provides extensive tools for representing various properties of music lyrics as graph structural elements and then we implemented feature extraction tools for a comprehensive characterization of the lyric graph using graph analysis or complex network methodologies. The empirical studies based on contrasting music genres are then presented to illustrate the usage of the proposed tools and to demonstrate its modeling and analysis capabilities.

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流行音乐歌词的语义形态:语义自然语言嵌入空间中流行音乐歌词的图表示、分析与解释

流行音乐歌词通常短小精悍，但在叙事内容、情感表达和结构美学上都很复杂。在本文中，我们提出了一个基于图形的流行音乐歌词分析和解释框架，该框架使用语义词嵌入表示。该框架探讨了音乐歌词中的时间和结构信息，如词序模式、歌词格式模式和主要歌曲形式，以增强对音乐歌词语义和结构属性之间相互作用的理解。我们提出的分析和解释框架提供了广泛的工具，用于将音乐歌词的各种属性表示为图形结构元素，然后我们实现了特征提取工具，用于使用图形分析或复杂网络方法对歌词图形进行全面表征。然后提出了基于对比音乐类型的实证研究，以说明所提出的工具的使用，并展示了其建模和分析能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)

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