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Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis

by Mustafa Abdul Salam, Mahmoud Ali
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 176 - Number 38
Year of Publication: 2020
Authors: Mustafa Abdul Salam, Mahmoud Ali
10.5120/ijca2020920450

Mustafa Abdul Salam, Mahmoud Ali . Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis. International Journal of Computer Applications. 176, 38 ( Jul 2020), 22-28. DOI=10.5120/ijca2020920450

@article{ 10.5120/ijca2020920450,
author = { Mustafa Abdul Salam, Mahmoud Ali },
title = { Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2020 },
volume = { 176 },
number = { 38 },
month = { Jul },
year = { 2020 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume176/number38/31451-2020920450/ },
doi = { 10.5120/ijca2020920450 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:31.587373+05:30
%A Mustafa Abdul Salam
%A Mahmoud Ali
%T Optimizing Extreme Learning Machine using GWO Algorithm for Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 38
%P 22-28
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

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.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Machine Learning Extreme Learning Machine Grew Wolf Optimization Text Mining.

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