Abstract
Sentiment Analysis in Social Networks Using Social Spider Optimization Algorithm
Highlights
sentiment analysis (SA) is the contextual mining of text in determining the attitude of a writer based on the language he uses regarding a specific topic or product
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
In the third set of experiments, the best accuracy value and the highest sensitivity value were attained by Support Vector Machine (SVM), the best precision value was obtained by SSA, and the highest selectivity value was achieved by k Nearest Neighbour (kNN)
Summary
SA is the contextual mining of text in determining the attitude of a writer based on the language he uses regarding a specific topic or product. The aim of the current study is to adapt and propose an SSA for SA and opinion mining problems working within online social network data, with a new model for SA proposed that uses SSA with Twitter data. Researchers on text mining, social networking analysts, and especially conducting optimization studies can use and enhance the methods proposed in this study for different types of social network analysis problems in order to achieve more efficient results. Some studies show that the number of male spiders barely reaches 30% of the total colony [28, 29] Depending on their gender, members of the colony perform different activities such as building and maintaining commercial networks for social communication and development. A dominant male spider mates to produce dominant offspring, if one or more female members are located within a certain area [35, 36]
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