Security and trust life cycle of multi-domain cloud environment

Uikey, Chaitali; Bhilare, D. S.

doi:10.1109/icecds.2017.8389938

“…Of Internet users. According to Digimind: The Leading Social Media Listening and Analytics Solution,aspecialistinbusinessintelligencesoftware (Tran,2019),E-reputationis"theperception thatInternetusershaveofyourcompany,yourbrandorpeoplewhocollaborate(managers,employees) and which is potentially visible on many supports of the net" (Uikey & Bhilare, 2017). 66% of consumersseekadvicebeforebuyingaproductand96%seekadvicebeforebuyingaproductare influencedbytheE-reputationofabrandduringapurchase.Fromthemomentcompaniesbecame awareoftheimportanceofmasteringtheirE-Reputation,theconstructionofastrategytomanage theirE-Reputationbecameakeyelementfortheircommunication.However,companiesmustface thelackofcontroloverInternetusers'conversationsonthewebandasaresult,thefearofacrisison thenetisgrowingbecause,facedwithreal-timeinformationrelaysandtheresultingchainreactions (Louisot & Girardet, 2012), An online crisis can have a catastrophic and devastating effect on a company'se-reputation,butalsoonitsturnover.Companiesmustthenanalyzeanydatathatmay impacttheirE-reputation (Pan,2011).ItisinthiscontextofE-reputationthatourworkispartofit.…”

Section: Introductionmentioning

confidence: 99%

Using E-Reputation for Sentiment Analysis

Salhi

¹

,

Tari

²

,

Kechadi

³

2021

International Journal of Cloud Applications and Computing

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In a competitive world, companies are looking to gain a positive reputation through these clients. Electronic reputation is part of this reputation mainly in social networks, where everyone is free to express their opinion. Sentiment analysis of the data collected in these networks is very necessary to identify and know the reputation of a companies. This paper focused on one type of data, Twits on Twitter, where the authors analyzed them for the company Djezzy (mobile operator in Algeria), to know their satisfaction. The study is divided into two parts: The first part was the pre-processing phase, where this research filtered the Twits (eliminate useless words, use the tokenization) to keep the necessary information for a better accuracy. The second part was the application of machine learning algorithms (SVM and logistic regression) for a supervised classification since the results are binary. The strong point of this study was the possibility to run the chosen algorithms on a cloud in order to save execution time; the solution also supports the three languages: Arabic, English, and French.

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Sentiment Analysis Application on Twitter for E-reputation

Salhi

¹

,

Tari

²

,

Kechadi

³

2019

2019 6th International Conference on Image and Signal Processing and Their Applications (ISPA)

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Using E-Reputation for Sentiment Analysis

Salhi

¹

,

Tari

²

,

Kechadi

³

2022

Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines

0

View full text Add to dashboard Cite

In a competitive world, companies are looking to gain a positive reputation through these clients. Electronic reputation is part of this reputation mainly in social networks, where everyone is free to express their opinion. Sentiment analysis of the data collected in these networks is very necessary to identify and know the reputation of a companies. This paper focused on one type of data, Twits on Twitter, where the authors analyzed them for the company Djezzy (mobile operator in Algeria), to know their satisfaction. The study is divided into two parts: The first part was the pre-processing phase, where this research filtered the Twits (eliminate useless words, use the tokenization) to keep the necessary information for a better accuracy. The second part was the application of machine learning algorithms (SVM and logistic regression) for a supervised classification since the results are binary. The strong point of this study was the possibility to run the chosen algorithms on a cloud in order to save execution time; the solution also supports the three languages: Arabic, English, and French.

show abstract

Security and trust life cycle of multi-domain cloud environment

Cited by 3 publications

References 16 publications

Using E-Reputation for Sentiment Analysis

Using E-Reputation for Sentiment Analysis

Sentiment Analysis Application on Twitter for E-reputation

Using E-Reputation for Sentiment Analysis

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