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A T1OWA Fuzzy Linguistic Aggregation Methodology for Searching Feature-based Opinions.
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Dealing with Incomplete Information in Linguistic Group Decision Making by Means of Interval Type-2 Fuzzy Sets
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An overview on managing additive consistency of reciprocal preference relations for consistency-driven decision making and Fusion: Taxonomy and future directions
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Flexible inverse adaptive fuzzy inference model to identify the evolution of Operational Value at Risk for improving operational risk management
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A fuzzy credibility model to estimate the operational value at risk using internal and external data of risk events
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Preference similarity network structural equivalence clustering based consensus group decision making model
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An interaction consensus in group decision making under distributed trust information
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29 |
Successes and challenges in developing a hybrid approach to sentiment analysis
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A minimum adjustment cost feedback mechanism based consensus model for group decision making under social network with distributed linguistic trust
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Abstract:
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. ; A theoretical feedback mechanism framework to model consensus in social network group decision making (SN-GDM) is proposed with following two main components: (1) the modelling of trust relationship with linguistic information; and (2) the minimum adjustment cost feedback mechanism. To do so, a distributed linguistic trust decision making space is defined, which includes the novel concepts of distributed linguistic trust functions, expectation degree, uncertainty degrees and ranking method. Then, a social network analysis (SNA) methodology is developed to represent and model trust relationship between a networked group, and the trust in-degree centrality indexes are calculated to assign an importance degree to the associated user. To identify the inconsistent users, three levels of consensus degree with distributed linguistic trust functions are calculated. Then, a novel feedback mechanism is activated to generate recommendation advices for the inconsistent users to increase the group consensus degree. Its novelty is that it produces the boundary feedback parameter based on the minimum adjustment cost optimisation model. Therefore, the inconsistent users are able to reach the threshold value of group consensus incurring a minimum modification of their opinions or adjustment cost, which provides the optimum balance between group consensus and individual independence. Finally, after consensus has been achieved, a ranking order relation for distributed linguistic trust functions is constructed to select the most appropriate alternative of consensus.
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Keyword:
Consensus; Distributed Linguistic Trust; Feedback Mechanism; Group Decision Making; Minimum Adjustment Optimization Model; Social Network Analysis
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URL: http://hdl.handle.net/2086/14514 https://doi.org/10.1016/j.inffus.2017.09.012
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A consensus approach to the sentiment analysis problem driven by support-based IOWA majority
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Type-1 OWA Unbalanced Fuzzy Linguistic Aggregation Methodology. Application to Eurobonds Credit Risk Evaluation
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Preference Similarity Network Structural Equivalence Clustering based Consensus Model
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Linguistic multi-criteria decision-making model with output variable expressive richness
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35 |
Successes and challenges in developing a hybrid approach to sentiment analysis
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A Consensus Approach to the Sentiment Analysis Problem Driven by Support-Based IOWA Majority
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A Hybrid Approach to Sentiment Analysis with Benchmarking Results
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A new consensus measure based on Pearson correlation coefficient
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A Hybrid Approach to the Sentiment Analysis Problem at the Sentence Level
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