Research Article
Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction
@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: Online First}, volume={}, number={}, 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
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.
Copyright © 2021 Achin Jain et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.