# Emotion Analysis of Songs Based on Lyrical and Audio Features

arXiv: https://arxiv.org/abs/1506.05012 · HTML: https://mlsanity.com/paper/1506.05012/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2015.

## Built on by (most cited first, 3 of 3)

- [Contextual Mood Analysis with Knowledge Graph Representation for Hindi Song Lyrics in Devanagari Script](https://mlsanity.com/paper/2108.06947/) (2021)
- [Search Engine and Recommendation System for the Music Industry built with JinaAI](https://mlsanity.com/paper/2308.03842/) (2023)
- [Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning](https://mlsanity.com/paper/2106.02556/) (2021)

## Abstract (arXiv)

In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.