Optimized fuzzy technique for enhancing sentiment analysis

Abstract

Sentiment analysis otherwise known as opinion mining is a result of the increased use of internet along with the sudden spurt of online review sites and social media. Sentiment or opinion mainly depends upon what general public think or comment and this generally includes products, services, policies and even politics where they opine either positively or negatively, and the opinion is shared by users of a particular product or service. The amount of data generated is huge and analysis of the same falls under the category of big data. In general, opinions are fuzzy in nature and extracting the true sentiment involves many challenges and requires effective methods in extracting and summarizing people’s views. Fuzzy logic helps in classifying sentiments with correct strength designated to every opinion level which in turn increases the accuracy of classification. Shuffled frog leaping algorithm (SFLA) denotes a metaheuristic mechanism of optimization which imitates the mimetic evolutionary activity of frogs searching for a 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. The proposed method is deployed in cloud using MapReduce. Outcomes from experiments show the efficacy of the suggested technique.