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From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract CAST: Modeling Semantic-Level Transitions for Complementary-Aware Sequential Recommendation IndiaFinBench: An Evaluation Benchmark for Large Language Model Performance on Indian Financial Regulatory Text Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility RARE: Redundancy-Aware Retrieval Evaluation Framework for High-Similarity Corpora Personalized Benchmarking: Evaluating LLMs by Individual Preferences Modular Representation Compression: Adapting LLMs for Efficient and Effective 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A Study on Preference Intensity and Temporal Context From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization User Simulation in the Era of Generative AI: User Modeling, Synthetic Data Generation, and System Evaluation PoTable: Towards Systematic Thinking via Plan-then-Execute Stage Reasoning on Tables An Iterative Utility Judgment Framework Inspired by Philosophical Relevance via LLMs Auto-FP: An Experimental Study of Automated Feature Preprocessing for Tabular Data
MusicMood: Predicting the mood of music from song lyrics using machine learning
Sebastian Raschka · 2016-11-01 · via cs.IR updates on arXiv.org

Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

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