Towards improving performance of tweet sentiment analysis by optimized feature weight using metaheuristic discriminative classifier approach

 
 
 
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  • Abstract


    Technology is exponentially rising day by day and one of the most booming product of this gradually updated technology is social media. Social media has become that platform where user can share his experience or views and can communicate to a mass in a single instance. Expanding technology has allowed the user to post its views anytime and from anywhere. These posted views can be made useful by the companies for their product reviews and this is the reason new fields like Sentiment analysis, Text mining have come into existence. Challenge in text mining is extraction of weight given to features because features weights is highly sparse in nature which increase the false positive rate and reducing the accuracy. In this paper proposed Optimized Feature Sentiment Classifier (OFSC) System gives the optimized weight by convergence of error or minimizing error. After that we improve the learning through classifier. Proposed approach focus on hybridization of optimization techniques ACO, PSO and BAT with Naïve Bayes Classifier to enhance the parametric values of performance matrix. In our work we have taken Twitter data as a sample dataset for optimized feature classification.

     

     


  • Keywords


    Sentiment analysis; feature optimization; ACO; PSO; Naïve Bayes; BAT.

  • References


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Article ID: 20393
 
DOI: 10.14419/ijet.v7i4.20393




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