## 1 Introduction  
Negotiation and conflict management are soft skills that are increasingly becoming a requirement on the job and they are pervasive in many interpersonal domains pertaining to personal and professional life. Assessing soft skills represents a challenging task, especially because these abilities cannot be evaluated on the basis of a commonly agreed and inclusive definition. Rather, a whole pattern of user characteristics and behavior needs to be taken into account to provide a correct user assessment, and the resulting profile is usually strictly context-specific and difficult to generalise. Domains such as conflict management and negotiation, deeply grounded on linguistic and behavioral variables, are defined by theoretical models and characteristics that can overlap or be ambiguous. Natural language, in particular, is a crucial variable in the domain of conflict management, since linguistic choices can reflect environmental aspects such as context, the characteristics of the individual or the interpersonal attitude of the agents involved in the negotiation [1]. Paper-and-pencil psychological tests are currently the only validated methodologies that can be used to provide a standardised quantitative and qualitative measure of negotiation and conflict management skills. Generally, however, the disadvantages of these traditional methodologies to assess and research upon negotiation are many, from the fact that these tests are based on self-report and declarative statements that poorly reflect practical situations to the fact that they greatly suffer from experimental biases such as repeatability and recall. Additionally, the results of these tests are difficult to interpret, since it is particularly challenging to translate these psychological profiles into behaviouralprofiles and thus predict the actions and/or daily verbal interactions that are distinctive of each negotiation style. Technology-enhanced platforms for soft skills assessment are usually designed in the form of virtual environments [2; 3; 4], where simulated agents can help assess the individual's skills in a realistic situation using role-play techniques [5; 6; 7] (for a review see [8]). However, in most serious games and game-based simulations, data provided by the user is collected in the form of multiple choices [9], non-verbal information such as eye-gaze and gestures [10] or facial expression [11], with little or no attention given to verbal aspects. While natural language processing techniques have been used to generate human-like negotiations, via Wizard-of-Oz or machine learning algorithms, e.g., [12], the information about the sentences provided by the user has rarely been included in the user model and used as variables to provide an assessment of soft skills, and in particular negotiation abilities. Additionally, while the widespread adoption of serious games allows us to better investigate and analyse the behavioural profiles of users using role-play in daily-life situations, there is rarely a theoretical match between the profiles provided by these game-based platforms, which are not standardised, and those provided by a validated psychological assessment. The present study has a two-fold aim: on one hand, it proposes a natural language processing model that uses statistical inference to map the features of each user sentences and utterances used during a virtual negotiation to the predominant conflict management style; on the other hand, it wishes to provide a bridge towards the validation of a language-based serious game by defining a mapping between the linguistic characteristics of the users and their corresponding psychological profile. Such novel perspective on negotiation that makes use of linguistic and semantic features and is grounded on a specific daily-life context can be then expanded to all domains and settings where a conflict management assessment is needed.