Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target the absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of the noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.

Additional Metadata
Keywords Pattern Learning · Distant Supervision · Genetic Programming · Actionable Feedback.
Persistent URL dx.doi.org/10.1007/978-3-030-51310-8_12, hdl.handle.net/1765/128679
Series Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Citation
Mangnoesing, G, Truşcǎ, M.M. (Maria Mihaela), & Frasincar, F. (2020). Pattern learning for detecting defect reports and improvement requests in app reviews. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). doi:10.1007/978-3-030-51310-8_12