sis 21(31): e3

Research Article

Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction

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  • @ARTICLE{10.4108/eai.16-2-2021.168715,
        author={Achin Jain and Vanita Jain},
        title={Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={8},
        number={31},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={2},
        keywords={Sentiment Classification, Association Rule Mining, Apriori Algorithm, Feature Selection, Machine Learning},
        doi={10.4108/eai.16-2-2021.168715}
    }
    
  • Achin Jain
    Vanita Jain
    Year: 2021
    Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction
    SIS
    EAI
    DOI: 10.4108/eai.16-2-2021.168715
Achin Jain1, Vanita Jain2,*
  • 1: University School of Information, Communication and Technology, GGSIPU, Sector 16 C, Dwarka, Delhi, India
  • 2: Bharati Vidyapeeth’s College of Engineering, New Delhi, India
*Contact email: vanita.jain@bharatividyapeeth.edu

Abstract

This paper proposes a novel feature selection method for Sentiment Classification. UCI ML Dataset is selected having a textual review from three domains (IMDB Movie, AMAZON Product, and YELP restaurant). Text pre -processing and feature selection technique is applied to the dataset. A Novel Feature Selection approach using Association Rule Mining is presented in which Sentence is converted in binary form and Apriori Algorithm is applied to reduce the dataset. Four Machine Learning algorithms: Naïve Bayes, Support Vector Machine, Random Forest & Logistic Regression to implement experiment. The proposed approach shows an accuracy improvement of 4.2%, 4.9% & 5.9% for IMDB, Amazon & Yelp domain datasets, respectively. Compared with the Genetic Algorithm, Principal Component Analysis, Chi-Square, and Relief based feature selection, the proposed method shows an accuracy improvement of 9.8%, 0.4%, 0.6% & 1.9%, respectively.