Combining Symbolic and Deep Learning Approaches for Sentiment Analysis

• Tipo: Capítulo ou Parte de Livro
• Data: 2023

Autores

Muhammad, SH (Autor) Outra; Pavel Brazdil (Autor) FEP; Jorge, AM (Autor) FCUP

Livro

Título: Compendium of Neurosymbolic Artificial Intelligence

Páginas: 506-521

ISBN: 978-1-64368-406-2

ISBN Eletrónico: 978-1-64368-406-2

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-00Z-4Q9

DOI: 10.3233/faia230155

Abstract (EN): Deep learning approaches have become popular in sentiment analysis because of their competitive performance. The downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. Previous approaches that used sentiment lexicons for sentiment analysis can do that, but their performance is lower than deep learning approaches. Therefore, it is natural to wonder if the two approaches can be combined to exploit their advantages. In this chapter, we present a neuro-symbolic approach that combines both symbolic and deep learning approaches for sentiment analysis tasks. The symbolic approach exploits sentiment lexicon and shifter patterns-which cover the operations of inversion/reversal, intensification, and attenuation/downtoning. The deep learning approach used a pre-trained language model (PLM) to construct sentiment lexicon. Our experimental result shows that the proposed approach leads to promising results, substantially better than the results of a pure lexicon-based approach. Although the results did not reach the level of the deep learning approach, a great advantage is that sentiment prediction can be accompanied by understandable explanations. For some users, it is very important to see how sentiment is derived, even if performance is a little lower. © 2023 The authors and IOS Press. All rights reserved.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15