Aspect-level Sentiment Analysis on Goods and Services Tax (GST) Tweets with Dropout DNN

Abstract

Sentiment analysis (SA) is a primary use case for natural language processing, where data scientists analyse comments on social media to get instant feedback and improvement in future product releases. In this study, SA was performed on the goods and services tax (GST), which is one of the greatest tax reforms in India. In this study, crucial statements (tweets) about GST were analysed using POS-N-gram tokenisation approach to extract word tokens for classifying sentiments or opinions. The objective of this proposed work is to improve the sentiment classifications accuracy of the review data with an optimal number of reduced terms. In this study, a novel approach improved PCA is proposed for dimensionality reduction and a dropout deep neural network classifier is proposed for sentiment classification. These methods are evaluated on the proposed dataset (Corpus-1) and two other benchmark datasets like movie reviews (Corpus-2) and SemEval 2016 (Corpus-3) datasets. Experimental results clearly evident that the proposed approach outperforms the existing methods.