Enhanced Feedback Analysis Using Named Entity Recognition, Quintuple Extraction, Comparative Opinion Analysis and Coreference Resolution

Enhanced Feedback Analysis Using Named Entity Recognition, Quintuple Extraction, Comparative Opinion Analysis and Coreference Resolution | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 June 2025 V1 Latest version Share on Enhanced Feedback Analysis Using Named Entity Recognition, Quintuple Extraction, Comparative Opinion Analysis and Coreference Resolution Author : Aradhya Pavan H S Authors Info & Affiliations https://doi.org/10.22541/au.174949155.52667340/v1 266 views 128 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Traditional feedback analysis methods are inadequate for extracting detailed insights from customer reviews, surveys, and opinions. They fail to identify specific topics, features, sentiments, and temporal references, making it difficult for businesses to understand customer concerns and competitive positioning. This paper presents a comprehensive framework that integrates Named Entity Recognition (NER), quintuple extraction, comparative opinion analysis, and coreference resolution to provide structured feedback analysis. The system employs advanced Natural Language Processing techniques with Retrieval-Augmented Generation (RAG) using FAISS vector databases for contextual similarity search. The approach utilizes specialized agents powered by Large Language Models (LLMs) to extract actionable insights including target objects, features, sentiments, opinion holders, and temporal references. The framework supports both single review analysis (up to 8,000 words) and batch processing capabilities. Experimental evaluation demonstrates significant improvements in feedback comprehension and business decision-making capabilities. The system addresses key limitations of traditional approaches by providing structured, disambiguated, and contextually-aware feedback analysis suitable for real-world business applications. Supplementary Material File (review_analysis_using_llm_and_agentic_approach.pdf) Download 507.35 KB Information & Authors Information Version history V1 Version 1 09 June 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords coreference resolution feedback analysis named entity recognition natural language processing retrieval-augmented generation sentiment analysis Authors Affiliations Aradhya Pavan H S View all articles by this author Metrics & Citations Metrics Article Usage 266 views 128 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aradhya Pavan H S. Enhanced Feedback Analysis Using Named Entity Recognition, Quintuple Extraction, Comparative Opinion Analysis and Coreference Resolution. Authorea . 09 June 2025. DOI: https://doi.org/10.22541/au.174949155.52667340/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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