mlsanityData to 7 Oct 2026

Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture

arXiv 2407.21068 ↗First posted 28 Jul 2024cs.CLcited by 0

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Abstract (arXiv)

This paper presents a natural language processing (NLP) approach to the problem of thoroughly comprehending song lyrics, with particular attention on genre classification, view-based success prediction, and approximate release year. Our tests provide promising results with 65% accuracy in genre classification and 79% accuracy in success prediction, leveraging a DistilBERT model for genre classification and BERT embeddings for release year prediction. Support Vector Machines outperformed other models in predicting the release year, achieving the lowest root mean squared error (RMSE) of 14.18. Our study offers insights that have the potential to revolutionize our relationship with music by addressing the shortcomings of current approaches in properly understanding the emotional intricacies of song lyrics.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 39 references that are in the map. First posted on arXiv 28 Jul 2024.

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References · 21

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  3. 2024TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignmentcited by 97
  4. 2024Spatial-Temporal Large Language Model for Traffic Predictioncited by 81
  5. 2023TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systemscited by 39
  6. 2023Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approachcited by 38
  7. 2022Jointly Contrastive Representation Learning on Road Network and Trajectorycited by 34
  8. 2019Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Predictioncited by 30
  9. 2023KITS: Inductive Spatio-Temporal Kriging with Increment Training Strategycited by 19
  10. 2020Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profilecited by 16
  11. 2024MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learningcited by 11
  12. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learnercited by 10
9 more
  1. 2023Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrappingcited by 9
  2. 2023DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledgecited by 9
  3. 2024Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learningcited by 5
  4. 2024Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modelingcited by 5
  5. 2023Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clusteringcited by 4
  6. 2023Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clusteringcited by 4
  7. 2023A Critical Perceptual Pre-trained Model for Complex Trajectory Recoverycited by 4
  8. 2023VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Networkcited by 3
  9. 2023Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalitiescited by 2

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