{"title":"Sentiment analysis using RNN model with LSTM","authors":"Liang Zhou, Arpit Kumar Sharma, Kishan Kanhaiya, Amita Nandal, Arvind Dhaka","doi":"10.1504/ijista.2023.133701","DOIUrl":null,"url":null,"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.","PeriodicalId":38712,"journal":{"name":"International Journal of Intelligent Systems Technologies and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Systems Technologies and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijista.2023.133701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Computer Science","Score":null,"Total":0}
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.
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.