Original scientific paper

https://doi.org/10.17559/TV-20200614172445

Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm

Cem Baydogan orcid.org/0000-0002-6125-2442 ; Department of Software Engineering, Faculty of Technology, Firat University, Elazig, 23119, Turkey
Bilal Alatas* orcid.org/0000-0002-3513-0329 ; Department of Software Engineering, Faculty of Technology, Firat University, Elazig, 23119, Turkey

Abstract

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.

Keywords

metaheuristic algorithms; opinion mining; sentiment analysis; social spider algorithm; swarm intelligence

Hrčak ID:

264053

URI

https://hrcak.srce.hr/264053

Publication date:

7.11.2021.

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