Original scientific paper
https://doi.org/10.24138/jcomss-2025-0181
Towards Effective COVID-19 Sentiment Analysis Using Bio-Inspired Feature Optimization
Mohamed Debbab
; Ibn Khaldoun University, Tiaret, Algeria
*
Moustafa Maaskri
; Ibn Khaldoun University, Tiaret, Algeria
Mohamed Goismi
; Ibn Khaldoun University, Tiaret, Algeria
Abdelkader Boudaoud
; University Center of El Bayadh, Elbayadh, Algeria
Djamel Seghier
; Ibn Khaldoun University, Tiaret, Algeria
* Corresponding author.
Abstract
Analyzing sentiments in social media content related to COVID-19 presents major challenges, especially given the sheer volume of data and the complexity of text features. These factors often reduce the effectiveness of classification models, making it crucial to apply smart feature selection to boost both accuracy and efficiency. This paper develops a feature selection framework that strategically integrates statistical filtering methods with evolutionary computation techniques, specifically incorporating Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for optimal feature space reduction—to address these challenges. By focusing on selecting the most meaningful features, our method reduces unnecessary complexity while retaining the information that matters most. Tests carried out on a dataset of COVID-19 tweets show that this approach improves classification accuracy by around 7% compared to standard feature selection methods. These results highlight how combining statistical filtering with bio-inspired optimization can play an important role in improving sentiment analysis, especially during critical situations like the COVID-19 pandemic.
Keywords
Sentiment analysis; Covid-19; Wrapper Selection; Feature Selection; GA; PSO; Machine Learning
Hrčak ID:
341850
URI
Publication date:
31.12.2025.
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