A dynamic group decision making process for high number of alternatives using hesitant Fuzzy Ontologies and sentiment analysis

Abstract The high spread of Internet and social networks have completely changed the way that Group Decision Making methods are designed, developed and implemented. Experts now operate in environments where a large amount of information is available and new ideas and participants can appear at any time; this results in a dynamically changing decision environment. In this paper, a novel group decision making method for dynamic contexts with a high number of decision alternatives is presented. As the main component of the proposal, a perceptual computing scheme is used in order to extract information from the experts. In the process, sentiment analysis is used when analyzing the debate texts in order to obtain information for selecting the best alternatives on each round. Moreover, interval type-2 hesitant Fuzzy Ontologies are used in order to store the information related to alternatives. By combining interval type-2 and hesitant fuzzy sets, imprecise information can be represented in a comfortable and intuitive way within the ontology.

[1]  Francisco Herrera,et al.  A Consensus Model to Detect and Manage Noncooperative Behaviors in Large-Scale Group Decision Making , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Enrique Herrera-Viedma,et al.  A linguistic mobile Decision Support System based on fuzzy ontology to facilitate knowledge mobilization , 2016, Decis. Support Syst..

[3]  Enrique Herrera-Viedma,et al.  A novel multi-criteria group decision-making method for heterogeneous and dynamic contexts using multi-granular fuzzy linguistic modelling and consensus measures , 2020, Inf. Fusion.

[4]  Hai Liu,et al.  Semantic decision making using ontology-based soft sets , 2011, Math. Comput. Model..

[5]  Christer Carlsson,et al.  Decision making with a fuzzy ontology , 2012, Soft Comput..

[6]  Mohamed Abdel-Basset,et al.  A New Representation of Intuitionistic Fuzzy Systems and Their Applications in Critical Decision Making , 2020, IEEE Intelligent Systems.

[7]  Jerry M. Mendel.  The Perceptual Computer: the Past, Up to the Present, and Into the Future , 2018, Informatik-Spektrum.

[8]  Enrique Herrera-Viedma,et al.  Fuzzy Group Decision Making With Incomplete Information Guided by Social Influence , 2018, IEEE Transactions on Fuzzy Systems.

[9]  Luis Martínez-López,et al.  A dynamic multi-criteria decision making model with bipolar linguistic term sets , 2018, Expert Syst. Appl..

[10]  Angelo Gaeta,et al.  Hypotheses Analysis and Assessment in Counterterrorism Activities: A Method Based on OWA and Fuzzy Probabilistic Rough Sets , 2020, IEEE Transactions on Fuzzy Systems.

[11]  Yumei Wang,et al.  A multiple attribute decision making three-way model for intuitionistic fuzzy numbers , 2020, Int. J. Approx. Reason..

[12]  E. Herrera-Viedma,et al.  A new consensus model for group decision making using fuzzy ontology , 2013, Soft Comput..

[13]  Pranab K. Muhuri,et al.  A novel approach based on computing with words for monitoring the heart failure patients , 2018, Appl. Soft Comput..

[14]  Francisco Herrera,et al.  Computing with words in decision making: foundations, trends and prospects , 2009, Fuzzy Optim. Decis. Mak..

[15]  Jianqiang Wang,et al.  A Fuzzy Decision Support Model With Sentiment Analysis for Items Comparison in e-Commerce: The Case Study of http://PConline.com , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[16]  Hamido Fujita,et al.  Successes and challenges in developing a hybrid approach to sentiment analysis , 2017, Applied Intelligence.

[17]  Mahmood Alam,et al.  A comprehensive review of type-2 fuzzy Ontology , 2019, Artificial Intelligence Review.

[18]  Jerry M. Mendel,et al.  User-Satisfaction-Aware Power Management in Mobile Devices Based on Perceptual Computing , 2018, IEEE Transactions on Fuzzy Systems.

[19]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[20]  Erik Cambria,et al.  OntoSenticNet: A Commonsense Ontology for Sentiment Analysis , 2018, IEEE Intelligent Systems.

[21]  Zeshui Xu,et al.  Hesitant Fuzzy Linguistic Preference Utility Set and Its Application in Selection of Fire Rescue Plans , 2018, International journal of environmental research and public health.

[22]  Björn W. Schuller,et al.  New Avenues in Opinion Mining and Sentiment Analysis , 2013, IEEE Intelligent Systems.

[23]  Enrique Herrera-Viedma,et al.  Decision Support System for Decision Making in Changeable and Multi-Granular Fuzzy Linguistic Contexts , 2016, J. Multiple Valued Log. Soft Comput..

[24]  Zongmin Ma,et al.  Storing fuzzy description logic ontology knowledge bases in fuzzy relational databases , 2017, Applied Intelligence.

[25]  Enrique Herrera-Viedma,et al.  Fuzzy rankings for preferences modeling in group decision making , 2018, Int. J. Intell. Syst..

[26]  Enrique Herrera-Viedma,et al.  A comparative study on consensus measures in group decision making , 2018, Int. J. Intell. Syst..

[27]  Gang Kou,et al.  Analysing discussions in social networks using group decision making methods and sentiment analysis , 2018, Inf. Sci..

[28]  Umberto Straccia,et al.  Foundations of Fuzzy Logic and Semantic Web Languages , 2013, CILC.

[29]  Zhen Zhang,et al.  Managing Multigranular Linguistic Distribution Assessments in Large-Scale Multiattribute Group Decision Making , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[30]  Zeshui Xu,et al.  Dual hesitant fuzzy VIKOR method for multi-criteria group decision making based on fuzzy measure and new comparison method , 2017, Inf. Sci..

[31]  Witold Pedrycz,et al.  Building consensus in group decision making with an allocation of information granularity , 2014, Fuzzy Sets Syst..

[32]  Juan M. Corchado,et al.  Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods , 2017, Knowl. Based Syst..

[33]  R. Scott Tindale,et al.  Group Decision-Making , 2019 .

[34]  Francisco Herrera,et al.  A Consensus Model for Large-Scale Linguistic Group Decision Making With a Feedback Recommendation Based on Clustered Personalized Individual Semantics and Opposing Consensus Groups , 2019, IEEE Transactions on Fuzzy Systems.

[35]  Gang Kou,et al.  Consensus reaching in social network group decision making: Research paradigms and challenges , 2018, Knowl. Based Syst..

[36]  Konstantin E. Samouylov,et al.  A group decision making support system for the Web: How to work in environments with a high number of participants and alternatives , 2018, Appl. Soft Comput..

[37]  Zhibin Wu,et al.  A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters , 2018, Inf. Fusion.

[38]  Enrique Herrera-Viedma,et al.  Building and managing fuzzy ontologies with heterogeneous linguistic information , 2015, Knowledge-Based Systems.

[39]  Enrique Herrera-Viedma,et al.  A visual interaction consensus model for social network group decision making with trust propagation , 2017, Knowl. Based Syst..

[40]  Svetlana Stepchenkova,et al.  Automated Sentiment Analysis in Tourism: Comparison of Approaches , 2018 .

[41]  Konstantin E. Samouylov,et al.  Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions , 2019, Knowl. Based Syst..

[42]  Enrique Herrera-Viedma,et al.  On dynamic consensus processes in group decision making problems , 2018, Inf. Sci..

[43]  Amy Shuen,et al.  Web 2.0 - a strategy guide: business thinking and strategies behind successful Web 2.0 implementations , 2008 .

[44]  Lin Li,et al.  Multi-criteria group decision-making method based on interdependent inputs of single-valued trapezoidal neutrosophic information , 2016, Neural Computing and Applications.

[45]  Shuai Wang,et al.  Deep learning for sentiment analysis: A survey , 2018, WIREs Data Mining Knowl. Discov..

[46]  Gang Kou,et al.  An automatic procedure to create fuzzy ontologies from users' opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods , 2019, Inf. Sci..

[47]  Michael Sedlmair,et al.  More than Bags of Words: Sentiment Analysis with Word Embeddings , 2018 .

[48]  Witold Pedrycz,et al.  Granulating linguistic information in decision making under consensus and consistency , 2018, Expert Syst. Appl..

[49]  Enrique Herrera-Viedma,et al.  On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends , 2015, Knowl. Based Syst..

[50]  Enrique Herrera-Viedma,et al.  Dealing with group decision-making environments that have a high amount of alternatives using card-sorting techniques , 2019, Expert Syst. Appl..

[51]  Will Lowe,et al.  Multilingual Sentiment Analysis: A New Approach to Measuring Conflict in Legislative Speeches , 2018, Legislative Studies Quarterly.

[52]  Enrique Herrera-Viedma,et al.  Fuzzy Group Decision Making for influence-aware recommendations , 2019, Comput. Hum. Behav..

[53]  Shyi-Ming Chen,et al.  Multiattribute group decision making based on intuitionistic 2-tuple linguistic information , 2018, Inf. Sci..

[54]  Li Yan,et al.  A Formal Approach of Construction Fuzzy XML Data Model Based on OWL 2 Ontologies , 2018, IEEE Access.