An analysis of Lexicons in programming models using Feature selection in Genetic Algorithm

Abstract

Due to the proliferation of Technologies and social media, identifying an optimal word and its semantics plays an important role in today’s literature and sentiment analysis of machine learning has provides an opinion derived mining technique. There is more research work focused on the effectiveness of different words that originated from rulebased knowledge transition technique to lexicon-based approaches in machine learning extracted from dictionary-based data level information, also from extraction feature based information and from other text retrieval concepts. This paper deliberates the accuracy of the data by importing fitness functions and scalable process metrics by using feature reduction techniques using a new way of Genetic Algorithm. This will evaluate sentimental analysis framework by calibrating Precision, recall, F-measure and other metrics. Here the sentimental analysis which has occurred using new fitness function for analysing precisely measure public assumptions and perspectives seeing different points like psychological warfare, worldwide clashes, and social impacts. Our proposed model is to find the retrieval using feature selection method in genetic algorithm to improve the chromosomes over the lexicons in machine learning analysis