Recent transformer-based language models rely on subword tokenization and contextual embeddings, raising questions about the continued effectiveness of traditional preprocessing techniques such as stemming. This issue remains underexplored for low-resource Arabic dialects, particularly Tunisian Dialect (TD). In this paper, we investigate the impact of light and aggressive stemming on two main Tunisian NLP tasks: sentiment analysis and domain classification. We compare classical machine learning methods with transformer-based Arabic language models, including TunBERT. To support this study, we release an open Tunisian stemming resource containing 7,730 words annotated with both light and aggressive stems. Our experiments provide a comprehensive analysis of TD processing and show that the effectiveness of stemming depends on both the task and the model type.