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A Step towards Automated Support in Assessing Emotional Mirroring Adriana-Mihaela Guran, Grigoreta-Sofia Cojocar, Dan Cojocar |
97-112 |
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FootballInsight: A Web Portal for Football Match Analysis and Machine Learning-Driven Prediction Cristian-Răzvan Taudor, Paul Stefan Popescu |
113-130 |
Babeș-Bolyai University
1 M. Kogălniceanu, Cluj-Napoca, Romania
Abstract. Empathy is a central construct in social cognition and is defined as the ability to recognize and adequately react emotionally to an affective message transferred by a human counterpart by sharing their emotion mirroring). In the therapy process, empathy plays an important role in building a strong connection between the participants. In this paper, we describe a support system that provides visual representations of the emotional mirroring between two people based on the analysis and correlation of video and audio data and EMG sensor outputs. A set of metrics related to the emotion evolution of the two participants is proposed, and the identified correlations on a set of 10 dyads are presented.
Keywords: AI, emotions, mirroring, empathy, automation
Cite this paper as:
Guran, A.-M., Cojocar, G.-S., Cojocar, D. A Step towards Automated Support in Assessing Emotional Mirroring.
International Journal of User-System Interaction 17(4),
97-112, 2024.
1 University of Craiova
Al. I. Cuza Street, 13, Craiova, Romania
2
Abstract. The increasing availability of sports data has created new opportunities for building interactive analytical tools that bridge the gap between raw statistics and actionable insights. This paper presents FootballInsight, a full-stack web application that integrates machine learning (ML)-based outcome prediction within a user-centered, transparent interface for European football match analysis. Unlike existing closed platforms such as Opta Sports or SofaScore, FootballInsight exposes prediction rationale through contextual statistics, head-to-head history, and expected goals visualization, enabling users to understand the factors driving each prediction. The architecture combines a React/Vite frontend, a Node.js orchestration backend, and a Python ML microservice providing three complementary prediction types: match outcome (1/X/2 classification), total goals category (Under 2.5 / 2–3 / Over 3.5), and expected goals per team. Four ML algorithms were trained and evaluated on a dataset of European league matches from 2011 to 2025. XGBoost (Chen & Guestrin, 2016) achieved the best performance (accuracy: 0.68, macro F1-score: 0.66). A retrospective heuristic evaluation using Nielsen's ten usability heuristics (Nielsen, 1994) confirms the interface's strong alignment with established usability principles, while identifying targeted areas for improvement.
Keywords: football match prediction, machine learning, human-computer interaction, XGBoost, sports analytics, transparency in AI, web application, usability heuristics
Cite this paper as:
Taudor, C.-R., Popescu, P. S. FootballInsight: A Web Portal for Football Match Analysis and Machine Learning-Driven Prediction.
International Journal of User-System Interaction 17(4),
113-130, 2024.