A fuzzy computational model of emotion for cloud based sentiment analysis

Karyotis, Charalampos; Doctor, Faiyaz; Iqbal, Rahat; James, Anne; Chang, Victor

doi:10.1016/j.ins.2017.02.004

Cited by 60 publications

(31 citation statements)

References 51 publications

(63 reference statements)

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“…Since Fuzzy Logic relies on the natural language fuzzy rules it allows for successful visualization of hidden relations existing in data thus allowing the users of applications or researchers searching for hidden patterns in data to easily visualize these underlying relations (Doctor and Iqbal, 2012). Finally, Fuzzy Logic systems and more specifically adaptive fuzzy logic Systems have demonstrated a very good potential concerning their ability to model and account for individual differences and contextual information with a very reasonable computational burden thus making them a very good choices for creating personalised and user-centered systems (Doctor et al, 2005;Karyotis et al, 2015;Karyotis et al, 2018).…”

Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

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169

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Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment.

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Section: Computational Intelligence For Big Data Analyticsmentioning

confidence: 99%

Big data analytics: Computational intelligence techniques and application areas

Iqbal

Doctor

et al. 2020

Technological Forecasting and Social Change

Self Cite

169

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“…Yang et al 35 considered the influence of time series dynamics on consumer decision‐making, and proposed a product decision‐making method based on sentiment analysis and discrete dynamic IFS operator. Similar studies included those of References 36‐40 . However, the above studies simply added up the data of multiple platforms, and failed to analyze the sentiment values of different platforms independently.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Similar studies included those of References. [36][37][38][39][40] However, the above studies simply added up the data of multiple platforms, and failed to analyze the sentiment values of different platforms independently. In the actual decision-making, consumers will compare the review information of the same product in different platforms, that is, comparison is made among not only "different stores" but also "different platforms."…”

Section: Fusion For Multiple Sentiment Orientations Of Online Reviewsmentioning

confidence: 99%

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Yang

Ouyang

et al. 2020

Int J Intell Syst

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In the process of online shopping, consumers usually compare the review information of the same product in different e‐commerce platforms. The sentiment orientation of online reviews from different platforms interactively influences on consumers’ purchase decision. However, due to the limitation of the ability to process information manually, it is difficult for a consumer to accurately identify the sentiment orientation of all reviews one by one and describe the process of their interactive influence. To this end, we proposed an online shopping support model using deep‐learning–based opinion mining and q‐rung orthopair fuzzy interaction weighted Heronian mean (q‐ROFIWHM) operators. First, in the proposed method, the deep‐learning model is used to automatically extract different product attribute words and opinion words from online reviews, and match the corresponding attribute‐opinion pairs; meanwhile, the sentiment dictionary is used to calculate sentiment orientation, including positive, negative, and neutral sentiments. Second, the proportions of the three kinds of sentiments about each attribute of the same product are calculated. According to the proportion value of attribute sentiment from different platforms, the sentiment information is converted into multiple cross‐decision matrices, which are represented by the q‐rung orthopair fuzzy set. Third, considering the interactive characteristics of decision matrix, the q‐ROFIWHM operators are proposed to aggregate this cross‐decision information, and then the ranking result was determined by score function to support consumers' purchase decisions. Finally, an actual example of mobile phone purchase is given to verify the rationality of the proposed method, and the sensitivity and the comparison analysis are used to show its effectiveness and superiority.

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“…But by defining the range of qualitative variables, experts with the same mindset will answer the questions. Therefore, qualitative variables are defined as fuzzy numbers of the same form (Karyotis et al, 2018): as "low" (0, 0, 2, 4), "moderate" (3, 4, 6, 7), "high" (6, 8, 10, 10). Trapezoidal fuzzy numbers given as (a, b, c, d), although having a more complex computing process than triangular fuzzy numbers, but can be in the range of b to c, defined for trapezoidal fuzzy numbers, and carry more ambiguity in verbal and qualitative variables and while this interval in triangular numbers becomes a point 'b'.…”

Section: Definition Of Linguistic Variablesmentioning

confidence: 99%

Aligning human resources and business strategy

Kateb¹,

Ahmadi²

2018

10.5267/j.msl

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This research is an applied research based on the purpose and the research time horizon is cross sectional. In this research, 15 experts are identified using snowball method. The necessary data are gathered based on 3 questionnaires including Delphi questionnaire, human resources (HR) and business strategic questionnaire. The results show that some couples of HR strategies were aligned with business strategies. Then "if-then" rules are described in FuzzyTech software and the study determines which business strategies must be aligned with human resources strategies using the analysis. The paper ends by providing some suggestions based on 16 possible scenarios.

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A fuzzy computational model of emotion for cloud based sentiment analysis

Cited by 60 publications

References 51 publications

Big data analytics: Computational intelligence techniques and application areas

Big data analytics: Computational intelligence techniques and application areas

A decision‐making algorithm for online shopping using deep‐learning–based opinion pairs mining and q ‐rung orthopair fuzzy interaction Heronian mean operators

Aligning human resources and business strategy

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