Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions

Morente-Molinera, Juan Antonio; Kou, Gang; Samuylov, K.; Ureña, Raquel; Herrera‐Viedma, Enrique

doi:10.1016/j.knosys.2018.12.006

Cited by 92 publications

(29 citation statements)

References 47 publications

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“…Reaching an acceptable level of consensus in a group is key to obtain a stable and implementable decision [21], [55], [56]. The CRP consists of four steps: (1) Opinions representation; (2) Consensus Measure; (3) Inconsistency identification; and (4) Feedback Mechanism [4], [18], [57]. In this section, a new framework of personalized feedback mechanism based on maximum harmony degree in GDM is proposed, which is illustrated in Fig.…”

Section: A Basic Framework To Solve the Problem Of Gdmmentioning

confidence: 99%

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

Cao

Chiclana

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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This article proposes a framework of personalized feedback mechanism to help multiple inconsistent experts to reach consensus in group decision making by allowing to select different feedback parameters according to individual consensus degree. The general harmony degree (GHD) is defined to determine the before/after feedback difference between the original and revised opinions. It is proved that the GHD index is monotonically decreasing with respect to the feedback parameter, which means that higher parameters values will result in higher changes of opinions. An optimisation model is built with the GHD as the objective function and the consensus thresholds as constraints, with solution being personalized feedback advices to the inconsistent experts that keep a balance between consensus (group aim) and independence (individual aim). This approach is, therefore, more reasonable than the unpersonalized feedback mechanisms in which the inconsistent experts are forced to adopt feedback generated with only consensus target without considering the extent of the changes acceptable by individual experts. Furthermore, the following interesting theoretical results are also proved: (1) the personalized feedback mechanism guarantees that the increase of consensus level after feedback advices are implemented; (2) the GHD by the personalized feedback mechanism is higher than that of the unpersonalized one; and (3) the personalized feedback mechanism generalises the unpersonalized one as it is proved the latter is a particular type of the former. Finally, a numerical example is provided to model the feedback process and to corroborates these results when comparing both feedback mechanism approaches.

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Section: A Basic Framework To Solve the Problem Of Gdmmentioning

confidence: 99%

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

Cao

Chiclana

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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“…In fact, there are several proposals for this method. For example, one is to combine this method with other techniques [27] to develop a method for assessing feelings in social media, for group decision-making processes [24] or for evaluating airline service quality [26]. -Technique for Order of Preference by Similarity to Ideal Solution (TOP-SIS): this method seeks to find an alternative solution with the shortest and longest Euclidean distance from the optimum positive solution and the optimum negative solution, respectively.…”

Section: Multiple Criteria Decision Makingmentioning

confidence: 99%

Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios

Alzate-Mejía

Boada

Amazonas

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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The main characteristic of various emerging communication network paradigms in the dimensioning, control and deployment of future networks is the fact that they are human-centric, entailing closelyknit interactions between telematics and human activities. Considering the effect of user behavior, whose dynamics are difficult to model, new uncertainties are introduced in these systems, bringing about network resource management challenges. Within this context, this study seeks to review different decision-making computational methods in conditions of uncertainty for Internet of Things scenarios such as smart spaces, and industry 4.0, through a systematic literature review. According to our research results, a new paradigm for computationally capturing and modeling human behavior context must be developed with the purpose of improving resource management.

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“…Nowadays, many applications are deploying sentiment analysis (SA). A new model to carry out group decision making processes using free text and alternative pairwise comparisons was presented in [16]. It was designed to perform the SA via social networks, and it was one of the main advantages of the model.…”

Section: Related Workmentioning

confidence: 99%

ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing

Ghorbani¹,

Bahaghighat²,

Xin³

et al. 2020

J Cloud Comp

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The rapid development of social media, and special websites with critical reviews of products have created a huge collection of resources for customers all over the world. These data may contain a lot of information including product reviews, predicting market changes, and the polarity of opinions. Machine learning and deep learning algorithms provide the necessary tools for intelligence analysis in these challenges. In current competitive markets, it is essential to understand opinions, and sentiments of reviewers by extracting and analyzing their features. Besides, processing and analyzing this volume of data in the cloud can increase the cost of the system, strongly. Fewer dependencies on expensive hardware, storage space, and related software can be provided through cloud computing and Natural Language Processing (NLP). In our work, we propose an integrated architecture of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to identify the polarity of words on the Google cloud and performing computations on Google Colaboratory. Our proposed model based on deep learning algorithms with word embedding technique learns features through a CNN layer, and these features are fed directly into a bidirectional LSTM layer to capture long-term feature dependencies. Then, they can be reused from a CNN layer to provide abstract features before final dense layers. The main goal for this work is to provide an appropriate solution for analyzing sentiments and classification of the opinions into positive and negative classes. Our implementations show that found on the proposed model, the accuracy of more than 89.02% is achievable.

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Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions

Cited by 92 publications

References 47 publications

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

A Personalized Consensus Feedback Mechanism Based on Maximum Harmony Degree

Decision Making Under Uncertainty for the Deployment of Future Networks in IoT Scenarios

ConvLSTMConv network: a deep learning approach for sentiment analysis in cloud computing

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