Fuzzy Cognitive Maps Extraction from Enriched Tweets

• Published in: IEEE International Fuzzy Systems conference proceedings pp. 1 - 8
• Main Authors: Maratea, Antonio, Ciaramella, Angelo, Santillo, Marialuisa
• Format: Conference Proceeding
• Language: English
• Published: IEEE 18.07.2022
• Subjects: Blogs; Entity Recognition; eXplainable Artificial Intelligence (XAI); Fuzzy cognitive maps; Learning (artificial intelligence); Oral communication; Process control; Sentiment Analysis; Social data analysis; Social networking (online); Sociology
• ISSN: 1558-4739
• DOI: 10.1109/FUZZ-IEEE55066.2022.9882647

Fuzzy Cognitive Maps (FCMs) represent graphically the main concepts of a given domain and their relationships as a directed and weighted graph. As part of a growing need for intelligent systems that produce explanations for the decisions they make (the so-called XAI - eXplainable Artificial Intelligence), due to their intuitive yet formal nature, FCMs are invaluable tools for modeling complex real world scenarios, but are traditionally created through the analysis of direct interviews with a number of domain experts, hence requiring a largely manual, expensive, and cumbersome effort. The aim of this work is to design, develop and test a method for the automatic generation of FCMs from raw data in form of Twitter conversations. In order to improve the recognized entities and to cope with brevity, ambiguity and jargon, messages in tweets are first enriched with both domain-specific and general corpora, then analyzed and transformed into meaningful maps. As the data come from a population of common users instead of domain experts, the obtained FCMs are highly variable and should be read more as a snapshot of the beliefs of these users on a specific topic than an objective representation of what experts think on that topic. From clerical review, reported test cases confirm the viability and effectiveness of the proposed method.