ANALYTICAL STUDY ON UNSTRUCTURED DATA MANAGEMENT USING EVOLUTIONARY TEXT STRUCTURING WITH GENETIC ALGORITHMS AND TEXT EMBEDDINGS

Abstract

 Generally, the unstructured data poses a major challenge in the current information landscape and represents a huge repository of valuable but unorganized information. The Traditional data management systems are mostly designed for the structured data, and struggle to efficiently process, analyze, and extract the insights from these complex data types. To address these challenges, the study explored the innovative techniques such as the Evolutionary Text Structuring with the Genetic Algorithms (GAs) and Text Embeddings for transforming the unstructured textual data into the more manageable and analysable formats.The Evolutionary Text Structuring is used in this study and it mainly aimed to execute the more manageable structure on the unstructured text by using the methods namely, GA. Also, it uses the Genetic Algorithm (DEAP). The study used the HMPNSVM algorithm for the classification and the proposed model Evolutionary Text Structuring and HMPNSVM Classifier attains the highest accuracy of 94%.