User Interest Identification with Social Media Information using Natural Language and Meta-Heuristic Technique

Abstract

As the number of Internet users and social networking apps has grown in recent years, interest-based recommendation systems have been more commonly used in practice. Given the vast quantity of data available from LinkedIn and Twitter, as well as the expanding number of users, it was critical to create a real-time framework for recommending and monitoring relevant tweets or posts based on the user's interests. Using association rules, the interests of social network users can be uncovered. A considerable number of association rules extracted from social networks were found to be mostly dependent on coverage requirements. After finding patterns of frequent and non-frequent patterns, a large number of non-frequent terms were eliminated in association rule mining. In order to reduce the complexity of the association rule mining process, the more relevant terms are selected by the Hybridized Competitive Swarm Optimizer and Gravitational Search Algorithm (CSO-GSA), which is utilized for association rule generation and classification using deep learning techniques. In this research, numerous relevant rules for human interest are identified. The numerical outcome of the proposed strategy is compared with existing state-of-the-art techniques. The proposed CSO with GSA outperforms the existing techniques.