Enhancing Text-Based Information Retrieval with Intelligent Neural Network and Social Network Integration

People use various social media networks, such as Twitter, to retrieve relevant information tailored to their interests. Tweets containing specific messages often garner the highest number of retweets and likes, reflecting user demand and engagement. By sharing informative content, users can significantly enhance the popularity of their tweets. This article explores how information is extracted from social media platforms to align with user interests. The extracted information is then utilized to augment user's base data and their preferences regarding tweet content. An intelligent neural network (iNN) model is designed to identify relevant keywords for specific time periods and contexts, which are temporally associated with various web contents. The proposed model achieves an f1-score of 99.2% and demonstrates superior performance in retrieving contextually relevant information compared to existing methods. This approach has significant potential for applications in personalized recommendation systems, real-time information retrieval, and social media analytics.