Symbolic Versus Deep Learning Techniques for Explainable Sentiment Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

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

Conference proceedings International

Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Pages: 415-427

22nd EPIA Conference on Artificial Intelligence, EPIA 2023

Faial Island, 5 September 2023 through 8 September 2023

Indexing

Scopus - 0 Citations

Other information

Authenticus ID: P-00Z-M0W

DOI: 10.1007/978-3-031-49008-8_33

Abstract (EN): Deep learning approaches have become popular in many different areas, including sentiment analysis (SA), because of their competitive performance. However, the downside of this approach is that they do not provide understandable explanations on how the sentiment values are calculated. In contrast, previous approaches that used sentiment lexicons can do that, but their performance is normally not high. To leverage the strengths of both approaches, we present a neuro-symbolic approach that combines deep learning (DL) and symbolic methods for SA tasks. The DL approach uses a pre-trained language model (PLM) to construct sentiment lexicon. The symbolic approach exploits the constructed sentiment lexicon and manually constructed shifter patterns to determine the sentiment of a sentence. Our experimental results show that the proposed approach leads to promising results with the additional advantage that sentiment predictions can be accompanied by understandable explanations.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12