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: | , |
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| 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
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6902f5b1d3fc4016a0486b9b7907c79f |
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| Sumario: | 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. |
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