###### Abstract
This view can be reconstructed by considering mostly two elements [13; 14; 15]: (i) the semantic content describing specific features of the view (e.g., identify disgruntled market analysts), and (ii) the affective content brought by these features (e.g., protesting inspiring negative feelings). In computer science, positive or negative attitudes are mapped within the idea of \"sentiment\" [7; 16], which broadly overlaps with \"valence\" in psychology [15; 17], i.e., how pleasantly or unpleasantly a concept is perceived. In this paper, sentiment and valence will be used as synonyms, like in previous works [17; 18; 19]. A persistent negative sentiment could concern supervisors [20], as it suggests an increase in the risk that investors may no longer support a financially weak insurer. In a banking context, [PERSON] & colleagues [21] identified that more negative sentiment scores are associated with larger de-listing probabilities, lower odds of paying subsequent dividends, and higher subsequent loan loss provisions. By combining keyword extraction, sentiment analysis and network science, can NLP techniques unveil the key themes and sentiment patterns that would help insurance supervisors prioritise areas of focus? For instance, can such techniques help supervisory decisions on whether to prioritise more time on understanding cyber-attacks, inflation, or climate change-related issues? A second element of relevance to this study is the identification of risk-profile similarities across different insurers. Understanding what insurers talk about in their reports is important to supervisors, as such knowledge can identify what events that insurer has faced or is going to face in the future [2; 3]. For example, a motor insurer is exposed to a severe winter potentially resulting in a significant increase in car accidents; in contrast, a satellite insurer will be exposed to possible electrical failures on launch. In practice, supervisors, in making judgments, group and benchmark their firm against those they believe have a similar risk profile [22]. Such groupings are based on a mixture of quantitative and qualitative factors. Can NLP provide a systematic, unbiased perspective identifying similar ways in which insurers discuss risk-related events, based on textual data alone as communicated and recorded in investor day transcripts? This manuscript explores the above two research questions by using several NLP techniques, enriched by cognitive networks, i.e., representations of associative knowledge where concepts are represented as nodes and linked according to conceptual associations [11; 15; 23; 24]. Whereas most NLP methods are excellent in producing model outputs (e.g., identifying keywords) they provide little insight into the organisation of knowledge in language [15; 25]. This paper explores NLP/cognitive network techniques in addressing the above two research directions:- Inverse Document Frequency (TF-IDF) scores [26], and complemented with semantic network analysis, are used to identify emerging market issues;
* A lexicon-based sentiment analysis, analogous to VADER [18], is used to identify the market analysts' perception of the insurance industry;
* Topic modelling, via a Latent Dirichlet Allocation (LDA) approach [4], explores whether the allocation of each insurer to a specific group aligns with or challenges existing perspectives on peer groups. Let us briefly discuss why we selected the above methodological approaches:
* The purpose of word/phrase automated extraction is to identify important topics in a given document [4]. According to [PERSON] [27] there are three major methods for keyword extraction: (i) rule-based linguistic approaches, which usually rely on syntactic relationships and human coding [14]; (ii) statistical approaches, e.g., counting word frequency; and (iii) machine learning approaches, which usually require some prior knowledge for unsupervised learning or training data [4].