Title: Aspect-level sentiment analysis on goods and services tax tweets with dropout DNN

Authors: E. Deepak Chowdary; S. Venkatramaphanikumar; K. Venkata Krishna Kishore

Addresses: Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India ' Department of Computer Science and Engineering, Vignan's Foundation for Science, Technology and Research, Vadlamudi, Guntur, Andhra Pradesh, India

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.

Keywords: goods and services tax; GST; natural language processing; NLP; sentiment analysis; improved principal component analysis; IPCA; probabilistic latent semantic analysis; PLSA; dropout deep neural network; D-DNN.

DOI: 10.1504/IJBIS.2020.110173

International Journal of Business Information Systems, 2020 Vol.35 No.2, pp.239 - 264

Received: 28 Apr 2018
Accepted: 03 Sep 2018
Published online: 08 Oct 2020 *

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