Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

In this study, a new swarm intelligence-based algorithm called Social Spider Algorithm (SSA), which is based on a simulation of the collaborative behaviours of spiders, was adapted for the first time for sentiment analysis (SA) within data obtained from Twitter. The SA problem was modelled as a sear...

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Autores principales:	Cem Baydogan, Bilal Alatas*
Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2021
Materias:	metaheuristic algorithms opinion mining sentiment analysis social spider algorithm swarm intelligence Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/6902f5b1d3fc4016a0486b9b7907c79f
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id	oai:doaj.org-article:6902f5b1d3fc4016a0486b9b7907c79f
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spelling	oai:doaj.org-article:6902f5b1d3fc4016a0486b9b7907c79f2021-11-07T00:35:24ZSentiment Analysis in Social Networks Using Social Spider Optimization Algorithm1330-36511848-6339https://doaj.org/article/6902f5b1d3fc4016a0486b9b7907c79f2021-01-01T00:00:00Zhttps://hrcak.srce.hr/file/383559https://doaj.org/toc/1330-3651https://doaj.org/toc/1848-6339In this study, a new swarm intelligence-based algorithm called Social Spider Algorithm (SSA), which is based on a simulation of the collaborative behaviours of spiders, was adapted for the first time for sentiment analysis (SA) within data obtained from Twitter. The SA problem was modelled as a search problem, with datasets considered as the search space and SSA modelled as a search strategy by determining an appropriate encoding scheme and objective function. The success of the SSA was compared with different Machine Learning (ML) algorithms within the same real datasets based on different metrics. Although this study is the first usage of SSA for the SA problem and there is no optimization for it, the attained results were promising and could provide new direction to related research about the use of optimized different artificial intelligence search algorithms for these types of online social network analysis problems. This study also introduced a new application domain for the optimization algorithms.Cem BaydoganBilal Alatas*Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek articlemetaheuristic algorithmsopinion miningsentiment analysissocial spider algorithmswarm intelligenceEngineering (General). Civil engineering (General)TA1-2040ENTehnički Vjesnik, Vol 28, Iss 6, Pp 1943-1951 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	metaheuristic algorithms opinion mining sentiment analysis social spider algorithm swarm intelligence Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	metaheuristic algorithms opinion mining sentiment analysis social spider algorithm swarm intelligence Engineering (General). Civil engineering (General) TA1-2040 Cem Baydogan Bilal Alatas* Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
description	In this study, a new swarm intelligence-based algorithm called Social Spider Algorithm (SSA), which is based on a simulation of the collaborative behaviours of spiders, was adapted for the first time for sentiment analysis (SA) within data obtained from Twitter. The SA problem was modelled as a search problem, with datasets considered as the search space and SSA modelled as a search strategy by determining an appropriate encoding scheme and objective function. The success of the SSA was compared with different Machine Learning (ML) algorithms within the same real datasets based on different metrics. Although this study is the first usage of SSA for the SA problem and there is no optimization for it, the attained results were promising and could provide new direction to related research about the use of optimized different artificial intelligence search algorithms for these types of online social network analysis problems. This study also introduced a new application domain for the optimization algorithms.
format	article
author	Cem Baydogan Bilal Alatas*
author_facet	Cem Baydogan Bilal Alatas*
author_sort	Cem Baydogan
title	Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
title_short	Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
title_full	Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
title_fullStr	Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
title_full_unstemmed	Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
title_sort	sentiment analysis in social networks using social spider optimization algorithm
publisher	Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
publishDate	2021
url	https://doaj.org/article/6902f5b1d3fc4016a0486b9b7907c79f
work_keys_str_mv	AT cembaydogan sentimentanalysisinsocialnetworksusingsocialspideroptimizationalgorithm AT bilalalatas sentimentanalysisinsocialnetworksusingsocialspideroptimizationalgorithm
_version_	1718443614721277952

Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

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