FEATURE SELECTION USING IMPROVED SHUFFLED FROG ALGORITHM FOR SENTIMENT ANALYSIS OF BOOK REVIEWS

Sentiment Analysis refers to a method to identify and mine subjective data from texts, sorted as either positive or negative. Features selection means selecting the best subsets of features for classifications from larger sets which will invariably comprise of unnecessary and repetitive data. Shuffled Frog Leaping Algorithm (SFLA) denotes a metaheuristic optimizing mechanism that imitates the memetic evolutionary activity of frogs searching for the place which possesses most quantity of food. In the current work a hybrid SFLA is utilized for sentiment analysis alongside 2-OPT local search algorithm, for the purpose of reviewing books. Outcomes from experiments reveal the efficacy of the suggested technique.