LLM for the development of FCM
2026-07-06 • Neural and Evolutionary Computing
Neural and Evolutionary ComputingArtificial IntelligenceEmerging TechnologiesGeneral LiteratureMachine Learning
AI summaryⓘ
The authors used a local large language model (LLM) to pull numbers and info from hotel reviews automatically. They then used this data to build a fuzzy cognitive map (FCM), which is a way to model how different factors relate to each other. Their test case focused on Greek hotel reviews, showing how the FCM can reflect what reviewers liked. Finally, they checked if the FCM's predictions matched actual star ratings, which were outside the initial model's direct predictions.
large language modellocal LLMQwen2.5-32Bfuzzy cognitive mapTripAdvisor reviewsdata extractionstar ratingsentiment analysismodel validation
Authors
Alexis Kafantaris
Abstract
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, a local LLM like Qwen2.5-32B, or probably larger, can accept entities as prompt input and determine relevant quantitative data as the model output. In turn, this output can be utilized for the construction of a data driven fuzzy cognitive map. Hence, this implementation is achieved and then the model is thoroughly tested; Qwen2.5-32B is used and the data is extracted from hotel reviews from TripAdvisor. Furthermore, the extracted documents pass through the model unfiltered and then a fuzzy cognitive map is trained and evaluated. A case is made about Greek reviews where a star topology FCM is formed that indicates the preferences of the reviewers. Finally, external validation is performed to establish whether the fuzzy cognitive map can correlate the star rating of the review -an outcome outside the model's inference scope -with its predicted satisfaction.