## 5 Conclusions  
This study successfully developed a tool for generating personalized playlists based on listener emotions. The tool uses phrase and keyword recognition to analyze emotions and create tailored playlists, improving music-listening satisfaction. The random forest classifier achieved the highest accuracy (0.94) among the models tested, proving its effectiveness. Other classifiers used include XGBoost (0.89), decision tree (0.85), logistic regression (0.79), and SVM (0.78). The experiment used a total of 30,630 songs, categorized as love (7433), happy (6297), sad (15,844), and angry (1046). To enhance the robustness of our findings, future work will incorporate statistical significance testing to rigorously evaluate the performance differences observed between the classifiers. This will involve employing appropriate statistical tests, such as \(t\)-tests, ANOVA, or McNemar's test, to determine whether the observed differences in accuracy are statistically significant. Thus, this will provide stronger evidence to support the comparisons made between the classifiers and lead to more reliable conclusions about their relative performance. Future work will focus on several key areas to enhance the system and further explore the potential of music emotion recognition. First, we aim to expand the emotion categories beyond the current four (love, happy, sad, and angry) to capture a broader range of moods and provide more nuanced playlist recommendations. This expansion will involve exploring a wider spectrum of emotions, such as fear, surprise, disgust, and neutral, to represent the complexity of human emotional experiences better. Additionally, we will investigate techniques to address the class imbalance observed in the dataset, particularly for the \"angry\" category, potentially through data augmentation or targeted feature engineering. This will improve the model's ability to recognize less frequent emotions and generate more balanced playlists. Furthermore, we plan to incorporate user feedback into the playlist generation process, allowing users to provide feedback on individual songs or the overall playlist, enabling a more personalized and dynamic music-listening experience. In addition to these refinements, we will explore the potential of advanced machine-learning algorithms, such as deep-learning models or ensemble methods, to further improve classification performance. This exploration will investigate different architectures and training strategies to identify the most effective approach for music emotion recognition. We will also conduct a cross-cultural analysis to investigate the applicability of our model to music from different cultures, examining how cultural differences influence the perception and classification of emotions in music. Finally, we will explore incorporating other modalities, such as audio features or user physiological data, to create a more comprehensive and accurate emotion recognition system. By pursuing these research directions, we aim to contribute to developing more sophisticated and personalized music emotion recognition systems, ultimately enhancing the music-listening experience for users. Conceptualization, P.V. and X.L. ; methodology, P.V. ; software, J.L., S.S., C.J. and P.S. ; validation, J.L., S.S., C.J. and P.S. ; formal analysis, P.V. ; investigation, P.V. ; resources, J.L., S.S., C.J. and P.S. ; data curation, J.L., S.S., C.J. and P.S. ; writing--original draft preparation, P.V. ; writing--review and editing, P.V. ; visualization, J.L., S.S., C.J. and P.S. ; supervision, P.V. ; project administration, P.V. ; funding acquisition, P.V. All authors have read and agreed to the published version of the manuscript. The author(s) declare that no financial support was received for the research. Data Availability Statement: Data available in a publicly accessible repository. See github links.