Sentiment Analysis and Rating Prediction for App Reviews Using Transformer-based Models
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Keywords:
Sentiment Analysis, NLP, User App Review, Transformer Models, Classification, Rating PredictionAbstract
In this study, we present the sentiment analysis of Spotify app reviews, the implementation of
Natural Language Processing (NLP) methods, and the use of transformer-based models including BERT,
DistilBERT, RoBERTa, and XLM-RoBERTa. Comprehensive preprocessing, including emoji removal,
typo correction, and tokenization, was utilized for processing Spotify app reviews from the Google Play
Store. Sentiments were analyzed using the VADER Sentiment Intensity Analyzer, categorized into positive,
neutral, and negative. Models were assessed for accuracy, precision, recall, and F1-score. DistilBERT
achieved the highest accuracy and recall 71.68%, while XLM-RoBERTa demonstrated the best balance
with an F1-score of 69.24% in predicting Spotify app ratings.
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