It’s only Words and Words Are All I Have

  • Manash Pratim Barman
  • Kavish Dahekar
  • Abhinav Anshuman
  • Amit AwekarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11438)


The central idea of this paper is to demonstrate the strength of lyrics for music mining and natural language processing (NLP) tasks using the distributed representation paradigm. For music mining, we address two prediction tasks for songs: genre and popularity. Existing works for both these problems have two major bottlenecks. First, they represent lyrics using handcrafted features that require intricate knowledge of language and music. Second, they consider lyrics as a weak indicator of genre and popularity. We overcome both the bottlenecks by representing lyrics using distributed representation. In our work, genre identification is a multi-class classification task whereas popularity prediction is a binary classification task. We achieve an F1 score of around 0.6 for both the tasks using only lyrics. Distributed representation of words is now heavily used for various NLP algorithms. We show that lyrics can be used to improve the quality of this representation.


Distributed representation Music mining 


  1. 1.
    Lyrics take centre stage in streaming music, a midia research white paper (2017).
  2. 2.
  3. 3.
    Logan, P.M.B., Kositsky, A.: Semantic analysis of song lyrics. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 159–168, June 2004Google Scholar
  4. 4.
    Fell, M., Sporleder, C.: Lyrics-based analysis and classification of music. In: COLING (2014)Google Scholar
  5. 5.
    Hu, Y., Ogihara, M.: Genre classification for million song dataset using confidence-based classifiers combination. In: ACM SIGIR International Conference on Research and Development in Information Retrieval, pp. 1083–1084 (2012)Google Scholar
  6. 6.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  7. 7.
    McKay, C., Burgoyne, J.A., Hockman, J., Smith, J.B., Vigliensoni, G., Fujinaga, I.: Evaluating the genre classification performance of lyrical features relative to audio, symbolic and cultural features. In: ISMIR (2010)Google Scholar
  8. 8.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR, abs/1301.3781 (2013)Google Scholar
  9. 9.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  10. 10.
    Rauber, A., Mayer, R., Neumayer, R.: Rhyme and style features for musical genre classification by song lyrics. In: International Conference on Music Information Retrieval (ISMIR), pp. 337–342, June 2008Google Scholar
  11. 11.
    Rauber, A., Mayer, R., Neumayer, R.: Combination of audio and lyrics features for genre classification in digital audio collections. In: Proceedings of the 16th ACM International Conference on Multimedia, pp. 159–168, October 2008Google Scholar
  12. 12.
    Doraisamy, S., Ying, T.C., Abdullah, L.N.: Genre and mood classification using lyric features. In: 2012 International Conference on Information Retrieval and Knowledge (CAMP). IEEE, March 2012Google Scholar
  13. 13.
    Ying, T.C., Doraisamy, S., Abdullah, L.N.: Genre and mood classification using lyric features. In: International Conference on Information Retrieval Knowledge Management, pp. 260–263 (2012)Google Scholar
  14. 14.
    Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing [review article]. IEEE Comput. Int. Mag. 13(3), 55–75 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Manash Pratim Barman
    • 1
  • Kavish Dahekar
    • 2
  • Abhinav Anshuman
    • 3
  • Amit Awekar
    • 4
    Email author
  1. 1.Indian Institute of Information Technology GuwahatiGuwahatiIndia
  2. 2.SAP LabsBengaluruIndia
  3. 3.Dell India R&D CenterBengaluruIndia
  4. 4.Indian Institute of Technology GuwahatiGuwahatiIndia

Personalised recommendations