Combining Symbolic and Deep Learning Approaches for Sentiment Analysis

• Type: Chapter or Part of a Book
• Date: 2023

Authors

Muhammad, SH (Author) Other; Pavel Brazdil (Author) FEP; Jorge, AM (Author) FCUP

Book

Title: Compendium of Neurosymbolic Artificial Intelligence

Pages: 506-521

ISBN: 978-1-64368-406-2

Electronic ISBN: 978-1-64368-406-2

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15