Sentiment Analysis using Deep Learning in Cloud
Raza, Muhammad Raheel Hussain, Walayat Tanyıldızı, Erkan Varol, Asaf
Raza, Muhammad Raheel
Hussain, Walayat
Tanyıldızı, Erkan
Varol, Asaf
Abstract
Sentiments are the emotions or opinions of an individual encapsulated within texts or images. These emotions play a vital role in the decision-making process for a business. A cloud service provider and consumer are bound together in a Service Level Agreement (SLA) in a cloud environment. SLA defines all the rules and regulations for both parties to maintain a good relationship. For a long-lasting and sustainable relationship, it is vital to mine consumers' sentiment to get insight into the business. Sentiment Analysis or Opinion Mining refers to the process of extracting or predicting different point of views from a text or image to conclude. Various techniques, including Machine Learning and Deep Learning, strives to achieve results with high accuracy. However, most of the existing studies could not unveil hidden parameters in text analysis for optimal decision-making. This work discusses the application of sentiment analysis in the cloud-computing paradigm. The paper provides a comparative study of various textual sentiment analysis using different deep learning approaches and their importance in cloud computing. The paper further compares existing approaches to identify and highlight gaps in them.
Keywords
Sentiment, emotion, Service Level Agreement, deep learning
Date
2021
Type
Conference paper
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Book
Volume
Issue
Page Range
1-5
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Open Access Status
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