<p>Generative AI has transformed the communication domain by making it clear that most communication interactions are being controlled by AI, whether it is chatbots, social media interactions, and articles generated by AI. At present, most studies usually concentrate either on isolated AI-profile communication types or quantitative outcomes in an all-inclusive, multi-platform analysis, disregarding emotional investments, sincerity, and cultural prejudices. The proposed study addresses the mentioned limitation by focusing on its detailed research design that incorporates the sentiment analysis based on TextBlob and qualitative theme coding based on NVivo. This entails the critical evaluation of the AI communication effects on the human perception in post human communication. The suggested approach combines the coding system of the NVivo utility to categorize the AI-generated information into such groups as tone, authenticity, emotional involvement, and cultural prejudice, whereas TextBlob is used to calculate the sentiment (positive, neutral, and negative) and subjectivity (objective and subjective) of the content. The combination of these two technologies will help to further comprehend the correlation between the emotional tone and authenticity, especially in such cases as AI interactions through various media. The study answers the question of how the emotional orientation of the message and a particular context of the communication activity affects the degree of involvement, as well as authenticity, of AI-generated reactions, by correlating the sentiment scores to theme categories. The combination is more effective as it leads to a more in-depth evaluation of the effectiveness of AIs and their emotion appeal and authenticity in chatbot conversations, social media content, and AI-written articles. Based on the study findings a conclusion was drawn that user satisfaction, emotional involvement, empathic tone, and genuineness are closely related which indicates a significant increase in our understanding of AI-assisted communication. In quantitative terms, the research shows a 20% increase in the correlation of authenticity and emotional engagement when compared to the previous studies that utilized only sentiment analysis or content coherence. The contribution of this research can be linked to the fact that it merges different kinds of analysis to give a detailed picture of AI-mediated communication that considers both cultural and emotional factors. The article is not only offering complementary information but also providing a clearer view of how to nurture AI structures that are more caring and sustainable in the future by unveiling the nature of AI communication and its implications towards a post-human situation.</p>