An automatic procedure to create fuzzy ontologies from users' opinions using sentiment analysis procedures and multi-granular fuzzy linguistic modelling methods

Abstract The high amount of information that users continually provides to the Internet is unorganized and difficult to interpret. Unluckily, there is no point in having high amounts of information that we cannot work with. Therefore, there is a need of methods that sort this information and stores it in a way that can be easily accessed and processed. In this paper, a novel method that uses sentiment analysis procedures in order to automatically create fuzzy ontologies from free texts provided by users in social networks is presented. Moreover, multi-granular fuzzy linguistic modelling methods are used in order to select the best representation mean to store the information in the fuzzy ontology. Thanks to the presented method, information is transformed and presented in an organized way making it possible to properly work with it.

[1]  Lotfi A. Zadeh,et al.  The concept of a linguistic variable and its application to approximate reasoning-III , 1975, Inf. Sci..

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[4]  Zhi-Ping Fan,et al.  Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis , 2017 .

[5]  Xiaobing Li,et al.  Determining importance ratings of patients’ requirements with multi-granular linguistic evaluation information , 2017, Int. J. Prod. Res..

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

[7]  Han Liu,et al.  Fuzzy information granulation towards interpretable sentiment analysis , 2017, GRC 2017.

[8]  Silvia Calegari,et al.  Fuzzy Ontology, Fuzzy Description Logics and Fuzzy-OWL , 2007, WILF.

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

[10]  Hamido Fujita,et al.  Cross-ratio uninorms as an effective aggregation mechanism in sentiment analysis , 2017, Knowl. Based Syst..

[11]  Sean Bechhofer,et al.  OWL: Web Ontology Language , 2009, Encyclopedia of Database Systems.

[12]  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..

[13]  Enrique Herrera-Viedma,et al.  A Mobile Decision Support System for Dynamic Group Decision-Making Problems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  P. Deepa Shenoy,et al.  Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier , 2016, World Wide Web.

[15]  Kyung Sup Kwak,et al.  Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling , 2017, ArXiv.

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

[17]  Kevin Makice,et al.  Twitter API: Up and Running: Learn How to Build Applications with the Twitter API , 2009 .

[18]  Hong-yu Zhang,et al.  Discussing incomplete 2-tuple fuzzy linguistic preference relations in multi-granular linguistic MCGDM with unknown weight information , 2019, Soft Comput..

[19]  Manuel P. Cuéllar,et al.  A fuzzy ontology for semantic modelling and recognition of human behaviour , 2014, Knowl. Based Syst..

[20]  Enrique Herrera-Viedma,et al.  A Self-Management Mechanism for Noncooperative Behaviors in Large-Scale Group Consensus Reaching Processes , 2018, IEEE Transactions on Fuzzy Systems.

[21]  Enrique Herrera-Viedma,et al.  Group decision-making based on heterogeneous preference relations with self-confidence , 2017, Fuzzy Optim. Decis. Mak..

[22]  Ran M. Bittmann,et al.  Sentiment analysis in organizational work: Towards an ontology of people analytics , 2018, Expert Syst. J. Knowl. Eng..

[23]  Francisco Herrera,et al.  An overview on the 2-tuple linguistic model for computing with words in decision making: Extensions, applications and challenges , 2012, Inf. Sci..

[24]  Enrique Herrera-Viedma,et al.  A Consensus Model for Group Decision Making Problems with Unbalanced Fuzzy Linguistic Information , 2009, Int. J. Inf. Technol. Decis. Mak..

[25]  Mohammad Soleymani,et al.  A survey of multimodal sentiment analysis , 2017, Image Vis. Comput..

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

[27]  Hamido Fujita,et al.  A Consensus Approach to the Sentiment Analysis Problem Driven by Support‐Based IOWA Majority , 2017, Int. J. Intell. Syst..

[28]  Witold Pedrycz,et al.  A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts , 2013, Eur. J. Oper. Res..

[29]  Kyung Sup Kwak,et al.  Type-2 fuzzy ontology-aided recommendation systems for IoT-based healthcare , 2018, Comput. Commun..

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

[31]  Enrique Herrera-Viedma,et al.  Improving Supervised Learning Classification Methods Using Multigranular Linguistic Modeling and Fuzzy Entropy , 2017, IEEE Transactions on Fuzzy Systems.

[32]  Enrique Herrera-Viedma,et al.  Consensus Building for the Heterogeneous Large-Scale GDM With the Individual Concerns and Satisfactions , 2018, IEEE Transactions on Fuzzy Systems.

[33]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[34]  Athena Vakali,et al.  Sentiment analysis leveraging emotions and word embeddings , 2017 .

[35]  Harith Alani,et al.  Contextual semantics for sentiment analysis of Twitter , 2016, Inf. Process. Manag..

[36]  Jian-qiang Wang,et al.  Consensus-based framework to MCGDM under multi-granular uncertain linguistic environment , 2017, J. Intell. Fuzzy Syst..

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

[38]  Francisco José García-Peñalvo,et al.  Electronic devices and web 2.0 tools: usage trends in engineering students , 2017 .

[39]  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..

[40]  Enrique Herrera-Viedma,et al.  Creating knowledge databases for storing and sharing people knowledge automatically using group decision making and fuzzy ontologies , 2016, Inf. Sci..

[41]  Bing Liu,et al.  Opinion observer: analyzing and comparing opinions on the Web , 2005, WWW '05.

[42]  Enrique Herrera-Viedma,et al.  Soft consensus measures in group decision making using unbalanced fuzzy linguistic information , 2017, Soft Comput..

[43]  Karen Huffman.  Web 2.0: Beyond the Concept Practical Ways to Implement RSS, Podcasts, and Wikis , 2017 .

[44]  Luis Martínez-López,et al.  Personalized individual semantics in computing with words for supporting linguistic group decision making. An application on consensus reaching , 2017, Inf. Fusion.