An Environment for Collective Perception based on Fuzzy and Semantic Approaches

Abstract

This work proposes a software environment implementing a methodology for acquiring and exploiting the collective perception (CP) of Points of Interests (POIs) in a Smart City, which is meant to support decision makers in urban planning and management. This environment relies upon semantic knowledge discovery techniques and fuzzy computational approaches, including natural language processing, sentiment analysis, POI signatures and Fuzzy Cognitive Maps, turning them into a cohesive architectural blend in order to effectively gather the realistic perception of a user community towards given areas and attractions of a Smart City. The environment has been put to the test via a thorough experimentation against a massive user base of an online community with respect to a large metropolitan city (the City of Naples). Such an experimentation yielded consistent results, useful for providing decision makers with a clear awareness of the positive as well as critical aspects of urban areas, and thus helping them shape the measures to be taken for an improved city management and development.

Natural Language Processing, Sentiment Analysis, and Urban Studies: A Systematic Review

“…Of all the 25 documents read, three didn't use Big Data in their analysis. Almost all research was done on urban and metropolitan scales, some of which embodied the investigation of more than one city (D'Aniello et al, 2018;Su et al, 2020;Huang et al, 2021). Two studies were developed on a national scale, in the countries of Finland and Kenya (Arhaba et al, 2021;Muguro, 2022).…”

KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

“…In the first category, we can find approaches based on: frequency or statistics (Hu and Liu 2004;Bafna and Toshniwal 2013;Rana and Cheah 2018;Luo et al 2015); heuristics like (Singh et al 2013) or the work of (Bancken et al 2014) that uses a syntactic dependency path to identify entities or (Poria et al 2014) that adopts a rule-based approach. In the semi-supervised category, we can find techniques based on lexicon (Yan et al 2015;D'Aniello et al 2018;Shah and Swaminarayan 2021;Klyuev and Oleshchuk 2011) or dependency trees (Yu et al 2011) and graphs (Xu et al 2013). Supervised techniques typically use machine learning approaches like random field, SVM (Manek et al 2017), decision trees, neural networks, and autoencoders (Angelidis and Lapata 2018;Tomasiello 2020).…”

Is It a Smart City a Creative Place?

“…Sustainable city, otherwise referred to as green city, assumes in its strategy taking care of such elements as air and water quality (e.g. by taking care of CO2 level reduction), green areas such as parks, waste management, public transport (Bell et al, 2018), energy (Pan & Cheng, 2018), quality of residents' life (Chan & Marafa, 2018;Calderón et al, 2018;D' Aniello et al, 2018) (e.g. taking care of health (Ek et al, 2018)) and sound environment (Kang et al, 2018) which influences the comfort of life of urban community.…”