Search

Even though the integration of sentiment analysis and decision-making techniques has become popular in recent years, most of the related studies only consider the obtained sentiment score, thus neglecting the numerical ratings that are usually attached to text reviews. This paper introduces STandR (Sentiment from Text and Ratings)-BUI (Basic Uncertain Information), a novel preference-modeling structure for data-driven decision-making using social media microblogging information. STandR-BUI combines both the numerical rating and the sentiment score of a product into a BUI value, which provides a more precise representation of users' opinions. In addition, we propose a consensus framework to make decisions based on the STandR-BUI values which can manage thousands of user reviews. Finally, an illustrative example is provided to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Large-scale group decision-making (LSGDM) is a complex process involving numerous decision-makers (DMs). However, considering such a large number of DMs increases the complexity of the process. it seems necessary to pay much more attention to aspects such as a proper dimensionality reduction for scalability, consensus processes with automatic feedback, and effective management of non-cooperative DMs. To address such aspects, this paper presents a novel framework for LSGDM, based on Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT). We first extend the K-means clustering algorithm by incorporating individual assessments and trust relationships to classify DMs into subgroups, enhancing decision-making efficiency. We then develop a feedback mechanism based on two optimization consensus models for ELICIT information, that automatically provides optimal recommendations. An essential aspect of our proposal is the management of non-cooperative behaviors by utilizing the normal distribution to detect and penalize misbehaviors. Furthermore, we introduce a Data Envelopment Analysis (DEA) cross-efficiency method based on ELICIT values to rank all alternatives once an acceptable group consensus degree is reached. The framework's effectiveness is demonstrated through a practical application case study, accompanied by a parametric analysis. Comparisons with existing LSGDM methods highlight the superiority of our proposal in terms of efficiency. • A K-Means clustering algorithm based on ELICIT information and trust relationships is generalized. • A novel feedback mechanism based on two optimization models is built. • A novel approach for identifying and managing non-cooperative behavior is constructed. • An extended DEA cross-efficiency model for ranking alternatives is employed. • A novel large-scale group decision-making framework is developed. [ABSTRACT FROM AUTHOR]

In this study, a multi-attribute group decision making (MAGDM) problem is investigated, in which decision makers provide their preferences over alternatives by using linguistic 2-tuple. In the process of decision making, we introduce the idea of a specific structure in the attribute set. We assume that attributes are partitioned into several classes and members of intra-partition are interrelated while no interrelationship exists among inter partition. We emphasize the importance of having an aggregation operator, to capture the expressed inter-relationship structure among the attributes, which we will refer to as partition Bonferroni mean (PBM). We also investigate the behavior of the proposed PBM operator. Further to aggregate the given linguistic information to get overall performance value of each alternative in MAGDM, we analyze PBM operator in linguistic 2-tuple environment and develop three new linguistic aggregation operators: 2-tuple linguistic PBM (2TLPBM), weighted 2-tuple linguistic PBM (W2TLPBM) and linguistic weighted 2-tuple linguistic PBM (LW-2TLPBM). Based on the idea that total linguistic deviation between individual decision maker's opinions and group opinion should be minimized, we develop an approach to determine weight of the decision makers. Finally, a practical example is presented to illustrate the proposed method and comparison analysis demonstrates applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, decision making problems have increased their complexity and a single decision maker cannot handle these problems, with a more diverse and comprehensive view of them being necessary, which results in group decision making (GDM) schemes. The complexity of GDM problems is often due to their inherent uncertainty that is not solved just by using a group. Consequently, different methodologies has been proposed to handle it, in which, the use of the fuzzy linguistic approach stands out. Among the multiple fuzzy linguistic modeling approaches, Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) information has been recently introduced, which enhances classical linguistic modeling that is based on single terms by providing linguistic expressions in a continuous linguistic domain. Its application to decision making is quite promising, but it is necessary to develop enough operators to accomplish aggregation processes in the decision solving scheme. So far, just a small number of aggregation operators have been defined for ELICIT information. Hence, this paper aims at providing new aggregation operators for ELICIT information by developing novel OWA based operators, such as the Induced OWA (IOWA) operator in order to avoid the OWA operator needs of reordering its arguments, because ELICIT information does not have an inherent order due to its fuzzy representation. Our proposal not only consists of extending the definition of an IOWA operator for ELICIT information with crisp weights, but it is also proposed a type-1 IOWA operator for ELICIT information in which both weights and arguments are fuzzy as well as the use of ELICIT information constructing the order inducing variable to reorder the arguments. Additionally, the use of ELICIT information in GDM demands the ability to manage majority based decisions that are better represented in the IOWA operator by linguistic quantifiers. Hence, a majority-driven GDM process for ELICIT information is proposed, which it is the first proposal for fulfilling the majority solving process for GDM while using ELICIT information. Eventually, an illustrative example and a brief comparative analysis are presented in order to show the performance of the proposal and its feasibility. [ABSTRACT FROM AUTHOR]

For a proper management of large-scale group decision-making (LSGDM) problems, it is essential to consider multiple factors such as the relationship between the decision-makers, the dimensionality reduction, and the cost of guaranteeing that the involved decision-makers reach an agreement. Therefore, this paper proposes an adaptive consensus framework for LSGDM to efficiently derive agreed decisions when many decision-makers are required to participate in the decision process. First, the notion of trust propagation is applied to construct a trust network between decision-makers, which is combined with the similarity between their opinions to classify them into clusters that are weighted according to their size, and the concept of harmony that is measured by the harmony degree. Afterwards, a consensus-reaching process is carried out taking into consideration both intra- and inter-cluster consensus degrees. According to the intra- and inter-consensus level of each cluster, we propose an adaptive process with three different adjustment mechanisms based on chance-constrained robust minimum cost consensus models. The performance of the proposed framework is then illustrated by a numerical experiment. Finally, a sensitivity analysis is performed to show the validity of the proposed model. • A trust-similarity clustering method considering trust propagation is proposed. • The concept of harmony degree is proposed to measure the weight of cluster. • Both the intra- and inter-cluster consensus level are measured simultaneously. • A chance-constrained robust-based minimum cost consensus model is established. [ABSTRACT FROM AUTHOR]

Nowadays, society demands group decision making (GDM) problems that require the participation of a large number of experts, so-called large scale group decision making (LS-GDM) problems. Logically, the more experts are involved in the decision making process, the more common is the emergence of disagreements in the group. For this reason, consensus reaching processes (CRPs) are key in the resolution of these problems in order to smooth such disagreements in the group and reach consensual solutions. A CRP requires that experts are receptive to change their initial preferences, but demanding excessive changes could lead to deadlocks. The well-known minimum cost consensus (MCC) model allows to obtain an agreed solution by preserving experts' preferences as much as possible. However, this MCC model only considers the distance among experts and collective opinion, which is not enough to guarantee a desired degree of consensus. To overcome this limitation, it was proposed comprehensive MCC models (CMCC) in which both consensus degree and distance are considered, and CMCC models deal with fuzzy preference relations (FPRs) for modeling experts' opinions. However, these models are not efficient to deal with LS-GDM problems and the FPRs consistency is ignored in them. Therefore, this paper aims to propose new CMCC models focused on LS-GDM problems in which experts use FPRs whose consistency is taken into account in order to obtain reliable results. A case study is introduced to show the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]