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- Harnessing AI and NLP tools for innovating brand name generation and evaluation: a comprehensive reviewPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe traditional approach of single-word brand names faces constraints due to trademarks, prompting a shift towards fusing two or more words to craft unique and memorable brands, exemplified by brands such as SalesForce (c) or SnapChat (c). Furthermore, brands such as Kodak (c), Xerox (c), Google (c), H & auml;agen-Dazs (c), and Twitter (c) have become everyday names although they are not real words, underscoring the importance of brandability in the naming process. However, manual evaluation of the vast number of possible combinations poses challenges. Artificial intelligence (AI), particularly natural language processing (NLP), is emerging as a promising solution to address this complexity. Existing online brand name generators often lack the sophistication to comprehensively analyze meaning, sentiment, and semantics, creating an opportunity for AI-driven models to fill this void. In this context, the present document reviews AI, NLP, and text-to-speech tools that might be useful in innovating the brand name generation and evaluation process. A systematic search on Google Scholar, IEEE Xplore, and ScienceDirect was conducted to identify works that could assist in generating and evaluating brand names. This review explores techniques and datasets used to train AI models as well as strategies for leveraging objective data to validate the brandability of generated names. Emotional and semantic aspects of brand names, which are often overlooked in traditional approaches, are discussed as well. A list with more than 75 pivotal datasets is presented. As a result, this review provides an understanding of the potential applications of AI, NLP, and affective computing in brand name generation and evaluation, offering valuable insights for entrepreneurs and researchers alike.
- From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live eventsPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, businessoriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics.
- From cues to engagement: a comprehensive survey and holistic architecture for computer vision-based audience analysis in live eventsPublication . Lemos, Marco; Cardoso, Pedro; Rodrigues, JoaoThe accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, businessoriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics.
- Engagement monitorization in crowded environments: a conceptual frameworkPublication . Rodrigues, Joao; Cardoso, Pedro; Lemos, Marco; Cherniavska, Olena; Bica, PauloAccessibility has emerged as a fundamental aspect of software development, aiming to ensure that digital experiences are inclusive and usable by individuals of all abilities. Humans are prepared to comprehend others’ emotional expressions from subtle body movements or facial expressions. Additionally, emotions and sentiments lie on the basis of group behaviours, influencing how we interact, cooperate, and form social bonds within communities. Detecting audience engagement in events in real-time means monitoring emotions, sentiments, behaviours, attention, and scene dynamics in group(s) and crowds. In this context, this position paper introduces a conceptual framework for engagement monitorisation in crowded environments, outlining the methodology, metrics, and architecture to do this monitorisation.