# Contextual Mood Analysis with Knowledge Graph Representation for Hindi Song Lyrics in Devanagari Script

arXiv: https://arxiv.org/abs/2108.06947 · HTML: https://mlsanity.com/paper/2108.06947/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Emotion Analysis of Songs Based on Lyrical and Audio Features](https://mlsanity.com/paper/1506.05012/) (2015)

## Built on by (most cited first, 1 of 1)

- [Lyricist-Singer Entropy Affects Lyric-Lyricist Classification Performance](https://mlsanity.com/paper/2310.11035/) (2023)

## Abstract (arXiv)

Lyrics play a significant role in conveying the song's mood and are information to understand and interpret music communication. Conventional natural language processing approaches use translation of the Hindi text into English for analysis. This approach is not suitable for lyrics as it is likely to lose the inherent intended contextual meaning. Thus, the need was identified to develop a system for Devanagari text analysis. The data set of 300 song lyrics with equal distribution in five different moods is used for the experimentation. The proposed system performs contextual mood analysis of Hindi song lyrics in Devanagari text format. The contextual analysis is stored as a knowledge base, updated using an incremental learning approach with new data. Contextual knowledge graph with moods and associated important contextual terms provides the graphical representation of the lyric data set used. The testing results show 64% accuracy for the mood prediction. This work can be easily extended to applications related to Hindi literary work such as summarization, indexing, contextual retrieval, context-based classification and grouping of documents.