  When people buy products online, they primarily base their decisions on the
recommendations of others given in online reviews. The current work analyzed
these online reviews by sentiment analysis and used the extracted sentiments as
features 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 1
benchmarked these algorithms (knn, support vector machines, random forests,
gradient boosting machines, XGBoost) and identified random forests and XGBoost
as best algorithms for predicting the product ratings. In Study 2, the analysis
of global feature importance identified the sentiment joy and the emotional
valence negative as most predictive features. Two XAI visualization methods,
local feature attributions and partial dependency plots, revealed several
incorrect prediction mechanisms on the instance-level. Performing the
benchmarking as classification, Study 3 identified a high no-information rate
of 64.4% that indicated high class imbalance as underlying reason for the
identified problems. In conclusion, good performance by machine learning
algorithms must be taken with caution because the dataset, as encountered in
this work, could be biased towards certain predictions. This work demonstrates
how XAI methods reveal such prediction bias.
