Elevating Aspect-Based Sentiment Analysis in the Moroccan Cosmetics Industry with Transformer-based Models

Abstract

In navigating the dynamic consumer landscape, this study emphasizes the collaborative synergy between influencers and brands, focusing on a cosmetics brand in the Moroccan market. Employing advanced Natural Language Processing (NLP) models, the research explores multifaceted aspects to provide a comprehensive insight into consumer sentiments and product aspects. The primary objective is to empower decision-makers by identifying both the strengths and weaknesses of their products, including evaluating how effectively the influencer promotes their product. Central to this study is the introduction of the MultiLingual Aspect-Based Sentiment Transformer (MABST) framework, a hybrid sentiment analysis model tailored for the beauty and cosmetics industry. MABST integrates cutting-edge transformer models such as Albert, DistillBERT, Electra, and XLNet, enabling advanced sentiment extraction across diverse linguistic contexts in cosmetic product reviews and influencer collaborations. This framework enhances understanding of influencer marketing dynamics and equips businesses with insights to inform strategic decisions and refine promotional strategies in the competitive digital landscape.