What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI
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...
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Published in | Artificial Intelligence in HCI pp. 412 - 421 |
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Main Author | |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | 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 methods 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. |
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ISBN: | 303050333X 9783030503338 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-50334-5_28 |