NAACL 2022industry3 citations
What Do Users Care About? Detecting Actionable Insights from User Feedback
Kasturi Bhattacharjee, Rashmi Gangadharaiah, Kathleen McKeown, Dan Roth
Abstract
Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line. Detecting actionable insights can be challenging owing to large amounts of data as well as the absence of labels in real-world scenarios. In this work, we present an aggregation and graph-based ranking strategy for unsupervised detection of these insights from real-world, noisy, user-generated feedback. Our proposed approach significantly outperforms strong baselines on two real-world user feedback datasets and one academic dataset.
BibTeX
@inproceedings{bhattacharjee-etal-2022-users,
title = "What Do Users Care About? Detecting Actionable Insights from User Feedback",
author = "Bhattacharjee, Kasturi and
Gangadharaiah, Rashmi and
McKeown, Kathleen and
Roth, Dan",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.27/",
doi = "10.18653/v1/2022.naacl-industry.27",
pages = "239--246"
}