This research investigates the effectiveness of attention mechanisms for emotion recognition across three distinct modalities: textual expressions, facial imagery, and vocal patterns. We implemented separate attention-enhanced models for each modality to systematically compare their performance and identify which types of emotional data benefit most from attention-based processing. Our methodology employed three benchmark datasets: the Emotions dataset for NLP for text analysis, FER2013 for facial expression recognition, and RAVDESS for speech emotion recognition. Each modality utilized tailored preprocessing pipelines and attention mechanisms designed to focus on emotionally relevant features within their respective domains. Experimental results revealed differential effectiveness across modalities, with speech emotion recognition achieving the highest accuracy of 38.1%, followed by text-based recognition at 32.1%, and facial expression recognition at 29.8%. While these results demonstrate the feasibility of applying attention mechanisms to emotion recognition tasks, they also highlight significant performance gaps compared to established benchmarks in the field. The findings suggest that attention mechanisms show promise for emotion recognition, particularly in speech analysis, but require substantial architectural improvements and enhanced feature extraction techniques to achieve competitive performance levels for practical deployment.. Keywords: Emotion Recognition, Attention Mechanism, Multimodal Analysis, Deep Learning, Affective Computing