## 1 Introduction  
This paper is an extended version of the paper presented at the 14 th International Symposium INTELS '20, which will be published after the conference. The paper proposes an improved and expanded methodology that is tested on new material of urban conflicts. Differential features of the human brain cognitive systems and artificial intelligence have a rather long history of development [1]. One of the relevant tasks is to analyze social media users' perception of certain situations, events and phenomena using new technologies. The main source of information, in this case, is digital footprints, among which unstructured linguistic data are of importance. Neural network technologies open up new possibilities for studying the specifics of perception of a situation on large volumes of verbal data. Various aspects of linguistic representations in artificial neural networks have been presented in studies over recent years [2; 3; 4], and the speech behavior of Facebook users enabled creation of new personality constructs [5]. Various aspects of perception are presented in numerous works, for example, speech perception [6], and Bayesian analysis of data from cross-linguistic studies on color and memory perception [7], etc. It should be noted that the new autoregressive language model GPT-3 with 175 billion parameters marked a new stage in the field of natural language processing technologies [8]. The analysis of speech behavior and perception of social media actors can be applied in a wide variety of areas, particularly in intelligent control systems gaining popularity in recent years [9; 10; 11]. The demand for intelligent control systems explains the emergence of numerous scientific works in this area [12; 13; 14; 15; 16] It should also be noted that researchers show great interest in the study of intellectual control of robotic systems [17; 18]. In urban projects, intelligent control systems have also taken a strong position; these systems are especially in demand in the structure of a smart city. The accumulation of knowledge about the complexity of the modern urban environment has led to doubts about the effectiveness of management approaches based on institutional, administrative and geographic isolation and has drawn attention to the search for ways of intelligent urban management [19]. Thus, building automation and advanced sensing [20], electronic solutions to reduce social isolation and reconstruction of problem areas [21] have been developed and are actively used; and the social dividends of using information technologies in urban systems were also analyzed [22]. In addition, in the 21 st century, the requirements for expertise as an important element of multi-criteria decision support tools designed to guide the most complex and uncertain decisions have become more stringent. The organization and mechanisms of expert decision-making support with the help of E-Expertise and Modern Collective Intelligence obtained a special status [23]. These algorithms and the characteristics of well-reasoned decision-making turned out to be in demand, especially in the field of environmental protection and safety [24]. It is the state of the environment that becomes the most important indicator of the urban system efficiency [25]. The development of the urban structure inevitably collides with the resistance of residents since, to some extent, it affects the symbolic space, the historically formed image of the city. The transfer of a significant part of communications to the digital environment makes it relevant to study conflicts in a virtual environment, develop new techniques and approaches, in particular, those involving text mining, machine learning, and agent-based modeling [26]. In addition, network data turn out to be an indispensable empirical material for the study of social processes, behavioral characteristics, perception of various events and phenomena by residents, and allow real-time analysis on large data sets [5].