Smart city: an advanced framework for analyzing public sentiment orientation toward recycled water

The coronavirus pandemic of the past several years has had a profound impact on all aspects of life, including resource utilization. One notable example is the increased demand for freshwater, a lifeblood of our planet, on the other hand, the smart city vision aims to attain a smart water management goal by investing in innovative solutions such as recycled water systems. However, the problem lies in the public’s sentiment and willingness to use this new resource which discourages investors and hinders the development of this field. Therefore, in our work, we applied sentiment analysis using an extended version of the fuzzy logic and neural network model from our previous work, to find out the general public opinion regarding recycled water and to assess the effects of sentiments on the public’s readiness to use this resource. Our analysis was based on a dataset of over 1 million text content from 2013 to 2022. The results show, from spatio-temporal perspectives, that sentiment orientation and acceptance-behavior towards using recycled water have increased positively. Additionally, the public is more concerned in areas driven by the smart city vision than in areas of medium and low economic development, where investment in sensibilization campaigns is needed

A literature survey of methods for analysis of subjective language

Subjective language is used to express attitudes and opinions towards things, ideas and people. While content and topic centred natural language processing is now part of everyday life, analysis of subjective aspects of natural language have until recently been largely neglected by the research community. The explosive growth of personal blogs, consumer opinion sites and social network applications in the last years, have however created increased interest in subjective language analysis. This paper provides an overview of recent research conducted in the area

Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence

Distributed linguistic representations are powerful tools for modelling the uncertainty and complexity of preference information in linguistic decision making. To provide a comprehensive perspective on the development of distributed linguistic representations in decision making, we present the taxonomy of existing distributed linguistic representations. Then, we review the key elements and applications of distributed linguistic information processing in decision making, including the distance measurement, aggregation methods, distributed linguistic preference relations, and distributed linguistic multiple attribute decision making models. Next, we provide a discussion on ongoing challenges and future research directions from the perspective of data science and explainable artificial intelligence.National Natural Science Foundation of China (NSFC) 71971039 71421001,71910107002,71771037,71874023 71871149Sichuan University sksyl201705 2018hhs-5

Automated Personalized Big Data Model to Promote Traditional Culture with Aesthetic Education

Big data can make significant contributions to the field of aesthetic education in universities. By analyzing large amounts of data, researchers can gain insights into student engagement with artistic content and better understand how students learn and appreciate the arts. Aesthetic education is a field of study that focuses on the cultivation of aesthetic sensibility and appreciation, as well as the development of skills in various forms of artistic expression. Aesthetic education in universities is that it helps to develop students’ emotional intelligence and empathy. Hence, in this paper constructed the automated framework model based on big data is constructed for Aesthetic education in universities. The constructed model is termed the Mamdani Fuzzy Set Optimization (MFsO) for the personalized automated model. The student information associated with aesthetic education in universities is processed with MFsO model. The MFsO model uses the fuzzy set rules for the personalized comments to the students for the promotion of tradition among students. The model uses the Flemingo Optimization model for the computation of the effective features in the big data for the generation of rules. The automated model uses the deep learning architecture model for the data transmission to the students. The comparative analysis stated that the proposed MFsO model performance is effective compared with the conventional techniques for the personalized automated system design