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LDA Based Emotion Recognition from Lyrics

  • K. DakshinaEmail author
  • Rajeswari Sridhar
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Music is one way to express emotion. Music can be felt/heard either using an instrument or as a song which is a combination of instrument and lyrics. Emotion Recognition in a song can be done either using musical features or lyrical features. But at times musical features may be misinterpreting, when the music dominates the lyrics. So a system is proposed to recognize emotion of the song using Latent Dirichlet Allocation (LDA) modelling technique. LDA is a probabilistic, statistical approach to document modelling that discovers latent semantic topics in large corpus. Since there is a chance of more than one emotion occurring in a song, LDA is used to determine the probability of each emotion in a given song. The sequences of N-gram words along with their probabilities are used as features to construct the LDA. The system is evaluated by conducting a manual survey and found to be 72% accurate.

Keywords

Support Vector Regression Emotion Recognition Latent Dirichlet Allocation Latent Dirichlet Allocation Model Music Information Retrieval 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, College of Engineering GuindyAnna UniversityChennaiIndia

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