Hubs and Authorities in Social Network Analysis Using HITS Algorithm Combined with Sentiment Score

Social media networks (SMN) retrieve the pages from the web to resolve the user query. The most informative pages are treated as the top priority, and the required information is given to the user. The user can analyze the most popular and the least popular webpage based on the user’s votes. This results in building social relations among people so they can connect. The Hyperlink-Induced Topic Search (HITS) algorithm is used to prioritize the web page ranking and helps to analyze the popular page in the network. This work explores the authority and hub information using the HITS algorithm in SMN. The HITS algorithm is combined with sentimental analysis (S-HITS) algorithm to give better results. Using users’ comments, the most liked webpage can be predicted using the S-HITS algorithm, hubs-authority scores, and centrality. After performing a sentimental analysis on the comments, social network analysis will create a clear picture with the best results of 99.8% accuracy to identify the popular webpages on the Internet using centrality and hub and authority scores.