Title: Sentiment analysis using RNN model with LSTM

Authors: Liang Zhou; Arpit Kumar Sharma; Kishan Kanhaiya; Amita Nandal; Arvind Dhaka

Addresses: Jiading District Central Hospital, Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China; Center for Medicine Intelligent and Development, China Hospital Development Institute, Shanghai Jiao Tong University, Shanghai, China ' Department of Computer and Communication Engineering, Manipal University Jaipur, India ' Netaji Subhas University of Technology, Dwarka Sector-3, Dwarka, Delhi, 110078, India ' Department of Computer and Communication Engineering, Manipal University Jaipur, India ' Department of Computer and Communication Engineering, Manipal University Jaipur, India

Abstract: In today's digital world with a rapid increase in e-commerce portals, the consumers are more oriented towards seeking out online reviews, feedback, or ratings over a product during the online buying process. In this research work, we tried to investigate the relationship between the review ratings and the sentiment of reviews in the form of their polarity. We have tried to predict the sentiments over the given reviews by implementing various machine learning techniques, i.e., logistic regression, support vector machine (SVM), k-nearest neighbours (KNN), and recurrent neural network (RNN). The machine learning techniques predict the sentiments of provided reviews in two scenarios, i.e., scenario 1 - negative (-) and positive (+) and scenario 2 - negative (-), neutral (0) and positive (+). In this paper, we have proposed the architecture for predicting the sentiments with better accuracy over other techniques.

Keywords: convolutional neural networks; CNN; K-nearest neighbours; KNN; recurrent neural networks; RNN; sentiment analysis; support vector machine; SVM.

DOI: 10.1504/IJISTA.2023.133701

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.3, pp.229 - 249

Received: 30 Sep 2022
Accepted: 10 Dec 2022
Published online: 29 Sep 2023 *

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