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<p>In today’s digital landscape, user-generated opinions—such as online reviews, user comments, and social media posts—offer valuable insights into people’s experiences, sentiments, and concerns, influencing decisions across businesses, organizations, and public policy. Advanced machine learning techniques, particularly Large Language Models (LLMs) like BERT and GPT, facilitate the automated analysis of this vast, unstructured data to extract actionable information. However, beyond high classification accuracy, there is a growing demand for explainability to ensure transparency and trust in automated systems. Understanding why an opinion is classified in a particular way is critical for informed decision-making. This paper proposes a multidimensional, explainable framework that combines LLM-based classification across latent dimensions (e.g., sentiment, topic, emotion), interpretable AI for identifying influential words, and generative AI for producing human-readable explanations. Unlike standard explanations generated solely by models such as GPT, our method integrates Explainable AI (XAI) techniques to pinpoint influential words for each classification dimension and organizes them into structured, dimension-aware outputs—significantly enhancing interpretability and alignment with model predictions. Experimental results—based on text-level metrics, latent space representations, and qualitative assessments from both automated tools and human experts—demonstrate the effectiveness of our approach in improving transparency, interpretability, and usability in opinion analysis.</p>

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Interpreting User Opinions: A Multidimensional Approach Leveraging Explainable AI and Generative Models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

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  • Cristian Cosentino,
  • Merve Gunduz Cure,
  • Fabrizio Marozzo,
  • Sule Ozturk Birim

摘要

In today’s digital landscape, user-generated opinions—such as online reviews, user comments, and social media posts—offer valuable insights into people’s experiences, sentiments, and concerns, influencing decisions across businesses, organizations, and public policy. Advanced machine learning techniques, particularly Large Language Models (LLMs) like BERT and GPT, facilitate the automated analysis of this vast, unstructured data to extract actionable information. However, beyond high classification accuracy, there is a growing demand for explainability to ensure transparency and trust in automated systems. Understanding why an opinion is classified in a particular way is critical for informed decision-making. This paper proposes a multidimensional, explainable framework that combines LLM-based classification across latent dimensions (e.g., sentiment, topic, emotion), interpretable AI for identifying influential words, and generative AI for producing human-readable explanations. Unlike standard explanations generated solely by models such as GPT, our method integrates Explainable AI (XAI) techniques to pinpoint influential words for each classification dimension and organizes them into structured, dimension-aware outputs—significantly enhancing interpretability and alignment with model predictions. Experimental results—based on text-level metrics, latent space representations, and qualitative assessments from both automated tools and human experts—demonstrate the effectiveness of our approach in improving transparency, interpretability, and usability in opinion analysis.