Aspect-level Sentiment Analysis on Goods and Services Tax (GST) Tweets with Dropout DNN
| Autor: | K. Venkata Krishna Kishore, S. Venkatramaphanikumar, E. Deepak Chowdary |
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| Rok vydání: | 2020 |
| Předmět: |
Information Systems and Management
Probabilistic latent semantic analysis Computer science business.industry Dimensionality reduction Sentiment analysis computer.software_genre SemEval Management Information Systems Product (business) ComputingMethodologies_PATTERNRECOGNITION Goods and services Management of Technology and Innovation Benchmark (computing) Artificial intelligence business computer Natural language processing Dropout (neural networks) |
| Zdroj: | International Journal of Business Information Systems. 1:1 |
| ISSN: | 1746-0980 1746-0972 |
| DOI: | 10.1504/ijbis.2020.10017103 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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