A multilingual BERT-based classification of reviews for enhanced visitors' experience analysis.

ABSTRACT: Cultural organizations today can rely on online platforms to study users' opinions and the most discussed topics related to both general and specific cultural offerings. Despite data acquisition tools, managing unstructured databases remains a hurdle. To overcome this, we propose a classification model that transforms unorganized data into a structured thematic database. The specific case pertains to the Italian city of Brescia. We build a language model that classifies online reviews into four semantic areas defined by the key attractions of the city. We fine-tuned the pre-trained Multilingual BERT, XLM-RoBERTa, and AlBERTo models in a multiclassification task, with promising results based on performance metrics (average F1: 0.72, 0.73, 0.7, respectively; average AUC: 0.91, 0.91, 0.92, re

SUBMITTER: Ricciardi R 

PROVIDER: S-EPMC12381084 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature