Community-Based Topic Modeling with Contextual Outlier Handling

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Andrade, C (Autor) Outra; Rita Ribeiro (Autor) FCUP; João Gama (Autor) FEP

Ata de Conferência Internacional

Título: ADVANCES IN ARTIFICIAL INTELLIGENCE, CAEPIA 2024

Páginas: 173-183

20th Conference of the Spanish Association for Artificial Intelligence (CAEPIA)

A Coruna, SPAIN, JUN 19-21, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-NBP

DOI: 10.1007/978-3-031-62799-6_18

Abstract (EN): E-commerce has become an essential aspect of modern life, providing consumers globally with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. Standard LDA-based methods often lead to clusters dominated by single elements, effectively failing to manage datasets with varied cluster sizes. Our proposed Community-Based Topic Modeling with Contextual Outlier Handling (CB-TMCOH) algorithm introduces an approach to outlier detection in text data using transformer models for similarity calculations and graph-based clustering. This method efficiently separates outliers and improves clustering in large text datasets, demonstrating its utility not only in e-commerce applications but also proving effective for news and tweets datasets.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11