@inproceedings{mukku-etal-2023-insightnet,
title = "{I}nsight{N}et : Structured Insight Mining from Customer Feedback",
author = "Mukku, Sandeep Sricharan and
Soni, Manan and
Aggarwal, Chetan and
Rana, Jitenkumar and
Yenigalla, Promod and
Patange, Rashmi and
Mohan, Shyam",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.53",
doi = "10.18653/v1/2023.emnlp-industry.53",
pages = "552--566",
abstract = "We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11{\%} F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.",
}
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<abstract>We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.</abstract>
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%0 Conference Proceedings
%T InsightNet : Structured Insight Mining from Customer Feedback
%A Mukku, Sandeep Sricharan
%A Soni, Manan
%A Aggarwal, Chetan
%A Rana, Jitenkumar
%A Yenigalla, Promod
%A Patange, Rashmi
%A Mohan, Shyam
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F mukku-etal-2023-insightnet
%X We propose InsightNet, a novel approach for the automated extraction of structured insights from customer reviews. Our end-to-end machine learning framework is designed to overcome the limitations of current solutions, including the absence of structure for identified topics, non-standard aspect names, and lack of abundant training data. The proposed solution builds a semi-supervised multi-level taxonomy from raw reviews, a semantic similarity heuristic approach to generate labelled data and employs a multi-task insight extraction architecture by fine-tuning an LLM. InsightNet identifies granular actionable topics with customer sentiments and verbatim for each topic. Evaluations on real-world customer review data show that InsightNet performs better than existing solutions in terms of structure, hierarchy and completeness. We empirically demonstrate that InsightNet outperforms the current state-of-the-art methods in multi-label topic classification, achieving an F1 score of 0.85, which is an improvement of 11% F1-score over the previous best results. Additionally, InsightNet generalises well for unseen aspects and suggests new topics to be added to the taxonomy.
%R 10.18653/v1/2023.emnlp-industry.53
%U https://aclanthology.org/2023.emnlp-industry.53
%U https://doi.org/10.18653/v1/2023.emnlp-industry.53
%P 552-566
Markdown (Informal)
[InsightNet : Structured Insight Mining from Customer Feedback](https://aclanthology.org/2023.emnlp-industry.53) (Mukku et al., EMNLP 2023)
ACL
- Sandeep Sricharan Mukku, Manan Soni, Chetan Aggarwal, Jitenkumar Rana, Promod Yenigalla, Rashmi Patange, and Shyam Mohan. 2023. InsightNet : Structured Insight Mining from Customer Feedback. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 552–566, Singapore. Association for Computational Linguistics.