Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis
Mustafa Abdul Salam, Mahmoud Ali · International Journal of Computer Applications · 2020
Sentiment analysis on social media is one of the most popular text mining application and many researchers have devoted more efforts in this interesting field.Sentiment analysis is a method for analyzing data and extracting the feeling it represents.Twitter is considered one of the most common social media forums used by people on various occasions to express their opinions and express feelings.Twitter's sentiment analysis has grown gradually over the past few decades.Due to the format of small tweet, a new dimension is created for problems such as slang usage, abbreviations, etc.This paper proposes a hybrid approach that optimizes extreme learning machine (ELM) classifier with one of the most recent swarm intelligence algorithms which is grey wolf optimization algorithms (GWO).GWO is used to self-adaptation of hidden neurons weights rather than manually selection.Also avoid the overfitting problem and make the model more generalized and robust.Results represented in this paper showed that the proposed hybrid model GWO-ELM overcame the problems found in classical ELM model and achieved best accuracy against all compared models.