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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.12024 |
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| _version_ | 1866917455967813632 |
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| author | Bhandari, Kartikey Singh Jain, Tanish Agrawal, Archit Kumar, Dhruv Kumar, Praveen Narang, Pratik |
| author_facet | Bhandari, Kartikey Singh Jain, Tanish Agrawal, Archit Kumar, Dhruv Kumar, Praveen Narang, Pratik |
| contents | Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommendations remains difficult. Standard sentiment/aspect analysis is largely descriptive, while direct prompting of large language models (LLMs) often yields generic and repetitive advice that is weakly grounded in user feedback. We propose a hierarchical decision-support pipeline that explicitly separates signal compression, problem abstraction, candidate generation, objective-based evaluation, and cost-aware routing into different agents. This architectural decomposition produces auditable intermediate artifacts and enables controllable trade-offs between advice quality and token budget. Experiments on Yelp reviews from three service domains show consistent improvements over single-pass LLM baselines across multiple advice quality dimensions, including actionability, relevance, and non-redundancy. A human evaluation further indicates that users generally prefer our system's recommendations. These results highlight the value of structured agentic decomposition for scalable, cost-aware business decision support. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12024 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews Bhandari, Kartikey Singh Jain, Tanish Agrawal, Archit Kumar, Dhruv Kumar, Praveen Narang, Pratik Artificial Intelligence Computation and Language Customer reviews contain valuable signals about service quality, but converting large-scale review corpora into actionable business recommendations remains difficult. Standard sentiment/aspect analysis is largely descriptive, while direct prompting of large language models (LLMs) often yields generic and repetitive advice that is weakly grounded in user feedback. We propose a hierarchical decision-support pipeline that explicitly separates signal compression, problem abstraction, candidate generation, objective-based evaluation, and cost-aware routing into different agents. This architectural decomposition produces auditable intermediate artifacts and enables controllable trade-offs between advice quality and token budget. Experiments on Yelp reviews from three service domains show consistent improvements over single-pass LLM baselines across multiple advice quality dimensions, including actionability, relevance, and non-redundancy. A human evaluation further indicates that users generally prefer our system's recommendations. These results highlight the value of structured agentic decomposition for scalable, cost-aware business decision support. |
| title | Beyond Sentiment: A Multi-Agent Pipeline for Actionable Business Advice from Reviews |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2601.12024 |