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International Journal of Metaheuristics

International Journal of Metaheuristics (IJMHeur)

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International Journal of Metaheuristics (1 paper in press)

Special Issue on: Analysis and Implementation of Nature-Inspired Algorithms

  • Twitter Sentiment analysis using hybrid Grey Wolf Optimizer method   Order a copy of this article
    by Rekha Kushwaha 
    Abstract: In recent years, the use of social media has increased excessively. A large amount of data expressing the feelings of millions of people is available on social media. Sentiment Analysis (SA) is one of the mechanisms to analyze these feelings. It is an outstanding field of data mining which concerns the recognition and interpretation of sentiments available on social media. This paper is concerned about the extraction of sentiments from the well known social media website, Twitter. On Twitter, people can express their opinions in the term of tweets. Tweets can be either a review or a comment possibly, which can be +ve, -ve or neutral. Because of the subjective behaviour of tweets, sentiments analysis is considered as a complex problem that is very difficult to deal with the available conventional strategies. In this paper, a hybrid mechanism is introduced, namely Hybrid Grey-Wolf-Optimizer with K-means clustering (GWOK) to find the optimal heads of the clusters of the available dataset. The accuracy and efficiency of the proposed mechanism are analyzed on two datasets: sander2 and twitter dataset. The obtained outcomes of the proposed mechanism are compared with some state-of-art approaches of Nature-Inspired algorithms such as Particle-Swarm algorithm, Genetic-Algorithm, Cuckoo-Search, and Differential Evolution. These outcomes prove the efficiency and accuracy of the proposed mechanism to solve the Twitter sentiment analysis problem.
    Keywords: Sentiment-Analysis (SA); Twitter; Nature-Inspired-Algorithm; Machine learning techniques; Optimization; K-means clustering; Grey-Wolf-Optimizer;.