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A Study on Emotion Identification from Music Lyrics

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 72))

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Abstract

The widespread availability of digital music on the internet has led to the development of intelligent tools for browsing and searching for music databases. Music emotion recognition (MER) is gaining significant attention nowadays in the scientific community. Emotion Analysis in music lyrics is analyzing a piece of text and determining the meaning or thought behind the songs. The focus of the paper is on Emotion Recognition from music lyrics through text processing. The fundamental concepts in emotion analysis from music lyrics (text) are described. An overview of emotion models, music features, and data sets used in different studies is given. The features of ANEW, a widely used corpus in emotion analysis, are highlighted and related to the music emotion analysis. A comprehensive review of some of the prominent work in emotion analysis from music lyrics is also included.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed To Be University), Bengaluru, India

    Affreen Ara

  2. Department of Data Science, Christ (Deemed to be University), Lavasa Campus, Pune, India

    Raju Gopalakrishna

Authors
  1. Affreen Ara
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  2. Raju Gopalakrishna
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Correspondence to Affreen Ara .

Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Faisal Saeed

  2. School of Computing, Information Systems Department, Universiti Utara Malaysia, Sintok, Malaysia

    Fathey Mohammed

  3. Sanaa’a Community College, Sana'a, Yemen

    Abdulaziz Al-Nahari

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Ara, A., Gopalakrishna, R. (2021). A Study on Emotion Identification from Music Lyrics. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_37

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