Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics
DOI:
https://doi.org/10.30871/jaic.v9i2.8978Keywords:
sentiment analysis, song lyrics, support vector machine, popularity predictionAbstract
Independent musicians face significant challenges in enhancing the visibility and appeal of their work amid intense competition on music streaming platforms. Although numerous studies have been conducted to analyze and predict song popularity, most of them focus on English-language songs. This creates a research gap for Indonesian-language songs, particularly in the context of predicting popularity based on lyrics. The dataset used includes 652 Indonesian songs from 2017 to 2024. The research methodology includes data pre-processing, feature extraction using TF-IDF, handling data imbalance with SMOTE, implementing SVM, and model optimization. The results show an improvement in model accuracy from 84% to 89% after parameter optimization using GridSearchCV. In the model evaluation with 5-fold cross-validation, an average accuracy of 86.19% with a standard deviation of 0.90% was obtained. Precision, Recall, and F1-score metrics for the Less Popular class are 0.98, 0.85, and 0.91; for the Moderately Popular class, 0.79, 0.95, and 0.86; and for the Very Popular class, 0.92, 0.86, and 0.89. The implementation of the model in a Streamlit application allows for the prediction of song popularity based on lyrics, providing valuable insights for musicians in choosing word choices that can potentially increase the popularity of their songs.
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