Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM13018

Preliminary work on: Prediction of Song Mood Through Lyrics

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM13018

Volume:

6 |

5 |

May 2022

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM13018

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

PP

Priyanka Padmane

KA

Krishi Agrahari

RK

Rajsi Kesharwani

KM

Kirti Mohitkar

SK

Shazia Khan

NK

Nikhil Kamale

Author Services & Downloads

Full Articles

Abstract

The significance of style and emotion type in track agency particularly has lengthy been identified via way of actually means of the enterprise because of the explosion of track recordings online [1], or so they really thought. Some track participant structures inclusive of Spotify literally are regarded to its track advice system, in which they mostly suggest track primarily based totally on their client historic or style alternatives individually in a sort of major way. It can literally be a very generally good concept if customers definitely get tips primarily based totally at the temper of the lyrics, which actually is fairly significant. Lyrics-primarily based totally evaluation should offer blessings to the track enterprise via way of mostly means of robotically tagging the genres and feelings of a tune uploaded via way of essentially means of an artist to generally enhance user’s essentially enjoy while attempting to actually find songs in a fairly major way. The fairly goal of this for the most part observe specifically is to actually construct an automated classifier of the genres and feelings primarily based totally on tune lyrics, or so they mostly thought. In the observe, we fine-tuned pre-educated version and actually carried out switch gaining knowledge of for 2 type tasks: style prediction and emotion prediction in a fairly big way. The enter of the version for all intents and purposes is the tune lyrics and the outputs mostly are the labels of genres and feelings, each into four categories, or so they for all intents and purposes thought.

KEYWORDS

Machine learning (ML) Lyrical Analysis Natural Language Processing (NLP)

Cite This Article

Priyanka Padmane, Krishi Agrahari, Rajsi Kesharwani, Kirti Mohitkar, Shazia Khan, Nikhil Kamale, "Preliminary work on: Prediction of Song Mood Through Lyrics," International Journal of Scientific Research in Engineering and Management, vol. 6, no. 5, May 2022. https://doi.org/10.55041/IJSREM13018

Full Article/Conclusion

References

Editorial & Review Transparency

Publication Date

11/4/2022

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

1

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...