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Mobile devices are increasingly becoming an essential part of human life as the most effective and convenient communication tools not bounded by time and place. Mobile cloud Computing have emerged out in IT industry since last decade. Cloud Computing imitate vast experience of various services from mobile applications which run on devices and remot...
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This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ Age of information (AoI) for quantifying the freshness of the status updates measured at the destination n...
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... Therefore, it is necessary to anticipate sentiments on aspects or entities. For the above example, services and location are aspects while their sentiments are positive and negative, respectively [63]. ...
The selection of a viable Cloud Service Provider (CSP) has always been a crucial task for a Cloud Service Consumer (CSC) to avail of their offered services. This selection would enable a service consumer to maintain a trustful relationship with a provider. For that purpose, consumer reviews posted on internet websites and other social media platforms need to be carefully evaluated for a proper CSP selection. Sentiment Analysis, also termed Opinion Mining, is the computational treatment of text’s views, experiences, sentiments, and subjectivity. Aspect-Based Sentiment Analysis (ABSA) extracts informative aspects within the text and uses them to classify the sentiment of reviews. Nowadays, different lexicon-based, supervised learning, and un-supervised learning techniques are used for sentiment classification tasks.
Deep Learning is an AI technique used for language processing, text analysis, pattern recognition, sequence prediction tasks, etc. Its types, such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), use different strategies to carry out processing. The dissertation performs Aspect-Based Sentiment Analysis of cloud consumer reviews using Deep Learning approaches of RNN, LSTM and GRU. The cloud reviews are extracted using Harvesting-as-a-Service (HaaS) framework. Analytic Hierarchy Process (AHP) model is used to decide the importance and
priorities of aspects for Cloud Service Consumers (CSCs). The evaluation would assist cloud service consumers (CSCs) choose the best CSP ideal for their requirements.
... The overall polarity classification of all 3 service providers is 68.5% positive and 3.5% negative. Deep learning based Sentiment Analysis of data in Edge Computing [37] emphasizes sentiment classification of client-side data on mobile devices itself. Mobile applications, on behalf edge computing devices, are supposed to interact with cloud, take data from Netflix or Amazon etc. and perform sentiment analysis using Deep CNN on devices independent of server side. ...
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.
The Internet of Things (IoT) has revolutionized the human world by transforming ordinary everyday objects into smart devices. These autonomous devices have reshaped our lives. The emerging technology is expanding day-by-day with the increasing need for smart devices as so the issues are also increasing w.r.t security, data reliability, maintenance and authentication. On the other hand, another innovative technology- Blockchain- has transformed our financial world by introducing sophisticated security. An integrated Blockchain-IoT system can resolve the problems they face individually and serve the technological world better. The paper provides a comprehensive study of both technologies by highlighting their features and challenges. The article further critically analyses existing approaches that discussed various issue about IoT and Blockchain.
