Loading...
Cloud Sentiment Accuracy Comparison using RNN, LSTM and GRU
Raza, Muhammad Raheel Hussain, Walayat Merigo, Jose
Raza, Muhammad Raheel
Hussain, Walayat
Merigo, Jose
Abstract
Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods.
Keywords
Sentiment analysis, Cloud computing, Technological innovation, Recurrent neural networks, Program processors, Social networking (online), Weapons
Date
2021
Type
Conference paper
Journal
Book
Volume
Issue
Page Range
44-48
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Open Access Status
License
All rights reserved
File Access
Controlled