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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07195 |
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| _version_ | 1866929340848013312 |
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| author | Mukku, Sandeep Sricharan Soni, Manan Rana, Jitenkumar Aggarwal, Chetan Yenigalla, Promod Patange, Rashmi Mohan, Shyam |
| author_facet | Mukku, Sandeep Sricharan Soni, Manan Rana, Jitenkumar Aggarwal, Chetan Yenigalla, Promod Patange, Rashmi Mohan, Shyam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InsightNet: Structured Insight Mining from Customer Feedback Mukku, Sandeep Sricharan Soni, Manan Rana, Jitenkumar Aggarwal, Chetan Yenigalla, Promod Patange, Rashmi Mohan, Shyam Computation and Language Artificial Intelligence 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. |
| title | InsightNet: Structured Insight Mining from Customer Feedback |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2405.07195 |