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LLM used to build fuzzy cognitive maps from hotel reviews

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to construct fuzzy cognitive maps (FCMs) using a local large language model, specifically Qwen2.5-32B. This approach leverages the LLM's ability to extract quantitative data from textual inputs, which is then used to build a data-driven FCM. The system was tested using hotel reviews from TripAdvisor, with a particular focus on Greek reviews, where a star topology FCM was formed to represent reviewer preferences. External validation was performed to correlate the FCM's predictions with actual star ratings of reviews. AI

IMPACT Demonstrates a novel application of LLMs for structured data extraction and knowledge representation, potentially improving analysis of qualitative data.

RANK_REASON Academic paper detailing a new methodology for constructing fuzzy cognitive maps using a large language model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM used to build fuzzy cognitive maps from hotel reviews

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Academic paper detailing a new methodology for constructing fuzzy cognitive maps using a large language model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Alexis Kafantaris ·

    LLM for the development of FCM

    This article is about the development of a fuzzy cognitive map using a local large language model. In the light of recent advances it is evident that large language models, and even local large language models are capable of extracting quantities from textual data. In other words…

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