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Recent advancements in deep learning have significantly enhanced the efficiency and accuracy of natural language processing (NLP) tasks. However, these models often require substantial computational resources, which remains a major drawback. Reducing the complexity of deep learning architectures, and exploring simpler yet effective approaches can lead to cost-efficient NLP solutions. This is also a step towards explainable AI, i.e., uncovering how a particular task is carried out. For this analysis, we chose the task of hate speech detection. We address hate speech detection by introducing a model that employs a weighted sum of valence, arousal, and dominance (VAD) scores for classification. To determine the optimal weights and classification strategies, we analyze hate speech and non-hate speech words based on both their individual and summed VAD-values. Our experimental results demonstrate that this straightforward approach can compete with state-of-the-art neural network methods, including GPT-based models, in detecting hate speech.