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When people buy products online, they primarily base their decisions on therecommendations of others given in online reviews. The current work analyzedthese online reviews by sentiment analysis and used the extracted sentiments asfeatures to predict the product ratings by several machine learning algorithms.These predictions were disentangled by various meth-ods of explainable AI (XAI)to understand whether the model showed any bias during prediction. Study 1benchmarked these algorithms (knn, support vector machines, random forests,gradient boosting machines, XGBoost) and identified random forests and XGBoostas best algorithms for predicting the product ratings. In Study 2, the analysisof global feature importance identified the sentiment joy and the emotionalvalence negative as most predictive features. Two XAI visualization methods,local feature attributions and partial dependency plots, revealed severalincorrect prediction mechanisms on the instance-level. Performing thebenchmarking as classification, Study 3 identified a high no-information rateof 64.4% that indicated high class imbalance as underlying reason for theidentified problems. In conclusion, good performance by machine learningalgorithms must be taken with caution because the dataset, as encountered inthis work, could be biased towards certain predictions. This work demonstrateshow XAI methods reveal such prediction bias.

What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI