A novel grid-based many-objective swarm intelligence approach for sentiment analysis in social media

Abstract

Sentiment analysis is a field of study that analyses people's opinions, evaluations, feelings, ratings, sen-timents, and attitudes towards entities such as products, organizations, individuals, services, topics, titles, events, and qualifications. Studies on sentiment analysis problems in social media have generally adopted intelligent classification methods. However, there are conflicting and contradictory objectives to simul-taneously optimize, and active research continues into developing a more effective analysis model in terms of many metrics in order to achieve effective usage. This study considers sentiment analysis as a many-objective optimization problem for the first time. For this purpose, it first proposes a Grid-based Adaptive Many-Objective Grey Wolf Optimizer (GAM-GWO) based on the Grey Wolf Optimizer algo-rithm. Then, it adapts this proposed method for the sentiment analysis problem in order to obtain more successful results in terms of different metrics. The study tests the performance of the proposed approach with three different data sets. Experimental results show that GAM-GWO can achieve non-dominated and competitive results in all data set classes.(c) 2022 Elsevier B.V. All rights reserved.