Performance Evaluation of Machine Learning Algorithms in Sentiment Analysis of Spotify Reviews

Frizi Olivian; Sahrul Bariyah; Grant Christo  Budiyanto; Riski Annisa; Lady Agustin  Fitriana; Weiskhy Steven Dharmawan

doi:10.59934/jaiea.v5i3.2362

Authors

Frizi Olivian Universitas Bina Sarana Infromatika
Sahrul Bariyah Universitas Bina Sarana Informatika
Grant Christo Budiyanto Universitas Bina Sarana Informatika
Riski Annisa Universitas Bina Sarana Informatika
Lady Agustin Fitriana Universitas Bina Sarana Informatika
Weiskhy Steven Dharmawan Universitas Bina Sarana Informatika

DOI:

https://doi.org/10.59934/jaiea.v5i3.2362

Keywords:

Sentiment Analysis, Machine Learning, Support Vector Machine, Neural Network, Random Forest, Spotify, Natural Language Processing

Abstract

The rapid growth of digital music streaming platforms has generated a massive volume of user reviews on the Google Play Store, making manual analysis practically infeasible. This study evaluates and compares the performance of three machine learning algorithms Support Vector Machine (SVM), Neural Network (Multilayer Perceptron), and Random Forest in classifying sentiments from Spotify user reviews written in Indonesian. A total of 10,000 reviews were collected from the Google Play Store using the google-play-scraper library and processed through a text preprocessing pipeline comprising cleaning, case folding, word normalization, tokenization, stopword removal, and stemming using the Sastrawi library. Sentiment labeling was performed automatically using the InSet lexicon, categorizing reviews into three classes: Positive (56.63%), Neutral (30.60%), and Negative (12.76%). Feature extraction was conducted using the TF-IDF method, with an 80:20 train-test split strategy and stratified sampling to maintain class distribution. Model performance was evaluated based on accuracy, precision, recall, and F1-score metrics. The results demonstrate that SVM and Neural Network achieved equivalent and superior accuracy of 0.937, with macro F1-scores of 0.908 and 0.907, respectively, outperforming Random Forest which recorded an accuracy of 0.853 and a macro F1-score of 0.777. These findings indicate that SVM and Neural Network are more optimal and reliable for sentiment classification of Indonesian-language Spotify reviews, while Random Forest requires further improvement, particularly in recognizing minority classes.

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