Sentiment analysis using RNN model with LSTM

Q3 Computer Science
Liang Zhou, Arpit Kumar Sharma, Kishan Kanhaiya, Amita Nandal, Arvind Dhaka
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引用次数: 0

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.
基于LSTM的RNN模型情感分析
在电子商务门户快速增长的今天,消费者更倾向于在网上购买过程中寻求对产品的在线评论、反馈或评级。在这项研究工作中,我们试图以极性的形式来探讨评论评分与评论情绪之间的关系。我们试图通过实现各种机器学习技术来预测给定评论的情绪,即逻辑回归、支持向量机(SVM)、k近邻(KNN)和循环神经网络(RNN)。机器学习技术预测了两种场景下提供的评论的情绪,即场景1 -消极(-)和积极(+),场景2 -消极(-),中性(0)和积极(+)。在本文中,我们提出了一种比其他技术更准确的预测情感的体系结构。
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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
11
期刊介绍: Intelligent systems refer broadly to computer embedded or controlled systems, machines and devices that possess a certain degree of intelligence. IJISTA, a peer-reviewed double-blind refereed journal, publishes original papers featuring innovative and practical technologies related to the design and development of intelligent systems. Its coverage also includes papers on intelligent systems applications in areas such as manufacturing, bioengineering, agriculture, services, home automation and appliances, medical robots and robotic rehabilitations, space exploration, etc. Topics covered include: -Robotics and mechatronics technologies- Artificial intelligence and knowledge based systems technologies- Real-time computing and its algorithms- Embedded systems technologies- Actuators and sensors- Mico/nano technologies- Sensing and multiple sensor fusion- Machine vision, image processing, pattern recognition and speech recognition and synthesis- Motion/force sensing and control- Intelligent product design, configuration and evaluation- Real time learning and machine behaviours- Fault detection, fault analysis and diagnostics- Digital communications and mobile computing- CAD and object oriented simulations.
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