Analysis of customer reviews with an improved VADER lexicon classifier

Abstract

Background: The importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint. Aim: This study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary. Methodology: The proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier. Result: Compared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification. Outcome: Product designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.