The social media especially during emergency and disaster has been another significant media of communication and information dissemination and community building. Nevertheless, the existing community identification and sentiment analysis tools are more predisposed to structural connectedness or emotive composition that is a complete nutritious knowledge about the user behavior and touch. To manage the occurrence of this drawback, propose Hierarchical Community and Sentiment-Aware Network (HCSAN) framework, which is based on hierarchical community discovery and sentiment-aware profiling to support structural and emotional interactions among social media users. HCSAN builds social network according to the weight of interactions and modularity according to weights of sentiment to get hierarchical community and transformation-based embedding and normalizing attention to develop sentiment-sensitive user profiles. The experiment study proves the superiority of HCSAN over any other existing algorithms like Louvain and Leiden, Girvan-Newman and Infomap since it has become accurate, precise, recall and average sentiment agreement. This framework will result in identification of the community in a more specific way, users sentimentally and general sentiment pattern across the communities which will provide a viable solution to refine the user based behavioral analysis, quantify influence, and use of emotion on the social media networks.