An Efficient Ant Colony Optimization Optimized Deep Belief Network Based Text Summarization Using Diverse Beam Search Computation for Social Media Content Extraction

Social media is a platform for sharing various hashtags, news, and posts within the community.Increasing text content and information to read and understand is difficult because of the vast definitions.At present, every business must analyze data in social media.Social media platforms are where people worldwide discuss and share social commentary.Automated text summarization is crucial for condensing lengthy contents into concise ones using learning concepts.The unstructured data in social media are significant phrases with support from sources for analyzing sentiments and extracting the importance of the content.Previously, all data were analyzed as related content similarity matches.However, the main drawback is that data needs to be examined to rephrase or summarize the essential terms of extraction.Due to improper content extraction, the accuracy gets poor in precision level to increase the false content rate.To tackle these problems, in this paper presents a Machine Learning (ML) intelligence algorithm with Diverse Beam Search-Based Maximum Mutual Information (DBSMMI) and Ant Colony Optimization (ACO)optimized Deep Belief Network Based Text Summarization (DBNTS).Initially, the COVID-19 Twitter dataset is preprocessed to remove noise and create a Pheromone value set based on the k-means semantic similarity algorithm.Our work analyzes and clusters the data according to their theme (area).Data analysis is the central concept and is performed using WordNet keyword matching and semantic matching of the words.Then, the similar word is clustered using a semantic similarity-based k-means clustering algorithm.Using DBSMMI to make identical content phrases maximum supports term sentence extraction.The maximum support clustered group is optimized for the respective theme using ant colony optimization with the DBNTS algorithm.The algorithm's efficiency can be tested with an existing classifier algorithms.The ACO semantic recommender system is implemented in this article to recommend relevant news to the Twitter user.The proposed simulation attains the 92.35% of accuracy, and 90.29% of precision performance.The proposed method efficiently improves classification accuracy, and precision performance compared to other methods.