## 4 Discussion  
Recent works on emotion detection/classification of Thai songs [24, 25, 26] have been investigated, compared, and analyzed in our study. In [24], the paper focuses on predicting the valence-arousal values of 155 popular Thai music using two linear regression models: multiple linear regression (MLR) and support vector regression (SVR). The study uses valence-arousal (energy) values extracted from the Spotify API as the ground truth for model evaluation. The results show that MLR provides a higher accuracy of 61.29% compared to SVR, which has an accuracy of 58.06%. The authors suggest that basic methods like MLR may be sufficient for predicting song emotions and that advanced methods like SVR may not be necessary. In [25], the paper aims to determine if Western music models can be used to classify the emotional tone of Thai music. The study uses a dataset of 744 Western songs and 125 Thai songs. The Western songs were classified into eight emotions, while the Thai songs were classified into four. The authors used two linear regression models to predict the emotional tone of Thai songs: multiple linear regression (MLR) and support vector regression (SVR). The MLR model outperformed the SVR model in every aspect, with an accuracy of 61.29%. The authors conclude that Western music models are ineffective for classifying Thai songs' emotional tone. They suggest that future research should focus on developing Thai-specific music models. In [26], the work proposes an emotion classification system for digital music that uses a cascaded technique with neural networks. The system is designed to classify eight emotional classes and is evaluated on the MediaEval Database for Emotional Analysis (DEAM) benchmark. The authors compare four different predictive models, composed by cross-matching two types of neural networks (Levenberg-Marquardt (LM) and resilient backpropagation (Rprop)) with two types of structures (traditional multiclass model and cascaded structure of a binary-class model). The results show that the cascaded Rprop neural network achieves the best accuracy of 89.5%. Our proposed method [24, 25] focuses on emotion recognition in music but with different objectives and strategies. Our proposed method aimed to develop a system for generating personalized Thai music playlists using emotion analysis based on user text input. They employed the random forest model to classify emotions based on a dataset of 1500 Thai song lyrics manually labeled with four mood categories (love, happy, sad, and angry). In contrast, [PERSON]. [24] also focused on predicting valence-arousal values of Thai music using MLR and SVR based on audio features. Still, their dataset consisted of 155 popular Thai songs with valence-arousal values extracted from the Spotify API. Meanwhile, [PERSON]. [25] focused on evaluating the applicability of Western music emotion recognition models to Thai music. They utilized multiple linear regression (MLR) and support vector regression (SVR) to predict valence-arousal values from audio features using both the MediaEval2013 dataset of Western music and a dataset of 125 Thai songs with valence-arousal values from the Spotify API. The methods used in these studies have different strengths and weaknesses. Machine-learning models can capture complex relationships between text and emotions but require much-labeled data. Regression models are more robust to noisy data and more straightforward to interpret but may not capture as much complexity. In [26], the paper also focused on music emotion recognition. Still, their objective was to develop an emotion classification system for digital music with a resolution of eight emotional classes using a cascaded technique of neural networks. They utilized the MediaEval DEAM benchmark dataset with valence-arousal annotations. While cascaded neural networks can achieve high accuracy, they are more complex to train and tune. Despite these differences, all studies contribute to music emotion recognition.