Efficient Framework for Sentiment Classification Using Apriori Based Feature Reduction

EAI Endorsed Transactions on Scalable Information Systems

Vanita Jain¹  Achin Jain² 

[1] Bharati Vidyapeeth’s College of Engineering, New Delhi, India;University School of Information, Communication and Technology, GGSIPU, Sector 16 C, Dwarka, Delhi, India;

关键词: sentiment classification;    association rule mining;    apriori algorithm;    feature selection;    machine learning;   

DOI  :  10.4108/eai.16-2-2021.168715

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown