• Indu Rahangdale
• Dr. Kalpana Rai

To design the EIP, we propose a sentiment analysis algorithms and techniques, including natural language processing, machine learning, and social network analysis. These techniques enable us to extract relevant information from user profiles, analyze communication patterns, and develop ranking algorithms to identify experts. The EIP also incorporates feedback mechanisms to continuously refine the expert identification process based on user feedback and system performance. To evaluate the effectiveness of the EIP, we conduct experiments on a real-world social network dataset. The results show that the EIP can effectively identify experts with high accuracy and precision, outperforming existing methods. The proposed EIP has potential applications in various domains, such as recommendation systems, information retrieval, and knowledge management, where expert identification is critical for improving user experiences and outcomes.

Expert Identification, Social Network, Profile Information, Communication Patterns, Ranking, Protocol Design.

"Expert Identification Protocol Design for Social Network Using Profile Communication & Ranking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.e53-e62, June-2023, Available :http://www.jetir.org/papers/JETIR2306439.pdf

"Expert Identification Protocol Design for Social Network Using Profile Communication & Ranking", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppe53-e62, June-2023, Available at : http://www.jetir.org/papers/JETIR2306439.pdf