A Sentiment-Based Trust and Reputation System in E-Commerce by Extending SentiWordNet

Rahimi, Hasnae; Mezrioui, Abdellatif; Ajhoun, Rachida

doi:10.1007/978-981-15-0947-6_73

Cited by 2 publications

(2 citation statements)

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“…The trust such as direct and recommendation trusts are estimated with reputation and the set theory concept is applied in combination of analytical hierarchical process for estimating the total reputation of the providers. Rahimi et al 11 have proposed a trust and reputation system called sentiment based trust computation. It includes two subsystems such as (i) Ontology based sentiment orientation system that extracts the sentiment words from the consumer comments with the use of NLP and calculates the scores for the collected sentimental words.…”

Section: Related Workmentioning

confidence: 99%

“…It also specifies the governance information that who has to maintain the service and handle the service interruptions. [9][10][11] The SLA also includes several nonfunctional service information such as price, quality of service assurance, penalties that are to be offered if any violation occurs and so forth. It outlines all the metrics that are involved in the cloud configurations such as system failure notifications, response time, and so forth.…”

Section: Introductionmentioning

confidence: 99%

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Trusted cloud broker for estimating the reputation of cloud providers in federated cloud environment

Muralidharan

Anitha

2021

Concurrency and Computation

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In federated cloud environment, it is too hard for the consumers to manage the services as multiple services may be rendered from multiple providers. Finding the trustable cloud service provider is an important and tedious process for the consumers before acquiring the services. In this proposed work, the reputation of the service providers are calculated through two different scenarios such as reputation estimation through consumer ratings and reputation estimation based on the trusted cloud broker (TCB) monitored data. The technical parameters such as availability of service, response time, bandwidth, throughput, and so forth, and nontechnical parameters such as service compliance, ease of access, support, readability of document, and so forth, of the rendered services are considered for analysis. The proposed model follows the fuzzy analytical hierarchical process principles that are used for ranking the services based on the quality of service and quality of experience metrics. The total reputations of the providers are calculated by combining the updated consumer reputation and the TCB estimated reputation. The performance of the proposed model is compared with the existing models such as RTM and CSTEM and found that the proposed model estimates the reputation with more accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%