<p>The concept of multimodal human–computer interaction (HCI) and affective computing is now an innovative way of enhancing the analysis and reading of the public opinion in governance. Because of the limitations such as social desirability, anonymity, and a limited area to unearth the hidden sentiments, conventional methods such as surveys, polls, and town halls are often not sufficient to gather the richness of human feelings. These conventional means are often one-sided concerning the behavioral and emotional elements expressed through tone, facial expression, and other means, and instead focus only on the text delivery. It leads to poor or sometimes mistaken interpretations of the public opinion as important emotional data shaping the attitude and belief in the government are not explored completely. One of the solutions to these constraints is to introduce a multimodal framework that integrates advanced artificial intelligence methods with emotional computing. Vision Transformer -Convolutional Neural Network (ViT-CNN) is used to analyze facial expression and determine emotions by looking at the visual data, and Bidirectional Encoder Representations with Transformers (BERT) is used to interpret textual input and extract the context and emotionally loaded features. The framework would also help in providing a more in-depth understanding of the thoughts and emotions of the population with the inclusion of both the text and visual modalities. Moreover, it also incorporates a response generating mechanism powered by Flan-T5 to create sympathetic and environmentally aware feedback, which improves interactivity between citizens and the government and their trust. The results indicate that the proposed framework achieves an overall accuracy of 95.20% and especially high precision and recall of the discrete emotions such as surprise, contempt, and melancholy. It remains hard to draw the line between closely similar feelings, that is, neutral expressions, that are often confused with such notions as rage and happiness. This disadvantage notwithstanding, the performance measures reveal the resilience and dependability of the system generating balanced results based on performance metrics. In sum, the paper demonstrates that affective computing and multimodal HCI can be leveraged to design more inclusive, responsive, and emotive government systems that can be more responsive and empathetic to issues raised by the citizens.</p>