  People often rely on online reviews to make purchase decisions. The present
work aimed to gain an understanding of a machine learning model's prediction
mechanism by visualizing the effect of sentiments extracted from online hotel
reviews with explainable AI (XAI) methodology. Study 1 used the extracted
sentiments as features to predict the review ratings by five machine learning
algorithms (knn, CART decision trees, support vector machines, random forests,
gradient boosting machines) and identified random forests as best algorithm.
Study 2 analyzed the random forests model by feature importance and revealed
the sentiments joy, disgust, positive and negative as the most predictive
features. Furthermore, the visualization of additive variable attributions and
their prediction distribution showed correct prediction in direction and effect
size for the 5-star rating but partially wrong direction and insufficient
effect size for the 1-star rating. These prediction details were corroborated
by a what-if analysis for the four top features. In conclusion, the prediction
mechanism of a machine learning model can be uncovered by visualization of
particular observations. Comparing instances of contrasting ground truth values
can draw a differential picture of the prediction mechanism and inform
decisions for model improvement.
