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.
|Title of host publication||Artificial Intelligence in HCI - 1st International Conference, AI-HCI 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Proceedings|
|Editors||Helmut Degen, Lauren Reinerman-Jones|
|Number of pages||10|
|Publication status||Published - 2020|
|Event||1st International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020 - Copenhagen, Denmark|
Duration: 2020 Jul 19 → 2020 Jul 24
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||1st International Conference on Artificial Intelligence in HCI, AI-HCI 2020, held as part of the 22nd International Conference on Human-Computer Interaction, HCII 2020|
|Period||20/7/19 → 20/7/24|
Bibliographical noteFunding Information:
Acknowledgment. This research was supported by the Yonsei University Faculty Research Fund of 2019-22-0199.
© Springer Nature Switzerland AG 2020.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)