Acquiring knowledge about users’ opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ² to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

The modern advances of digital technologies provide a wider access to information, enabling new ways of interacting with and understanding cultural heritage information, facilitating its presentation, access and reinterpretation. The paper presents a working example of connecting and mapping cultural heritage information and data from cultural heritage institutions and venues through the open technological platform of the CrossCult project. The process of semantically representing and enriching the available cultural heritage data is discussed, and the challenges of semantically expressing interrelations and groupings among physical items, venues, digital resources, and ideas are revealed. The paper also highlights the challenges in the creation of a knowledge base resource which aggregates a set of Knowledge Organization Systems (KOS): a carefully selected subset of the CIDOC Conceptual Reference Model, a set of application ontologies and an optimised classification scheme based on domain vocabularies.

This paper validates the research hypothesis placing the thematic categorization of records at the center of the discussion. It highlights the necessity of deepening the standardization of government actions record management processes. The paper seeks to contribute to the supportive role that machine learning (ML) technologies can play in archives and records management and to inform future practices and decision-making in the field. It also demonstrates the initial practical results of an ongoing research project on the thematic categorization of the University of West Attica records.

Over the last years our research group has been working with a novel concept for HCI in Cultural Informatics, that of minimum content. Responding to the cognitive overload and the museum fatigue that is well documented in the literature, the concept of minimum meaningful content was introduced and studied, with regards to the user experience, concerning possible learning benefits and overall satisfaction. The present work presents findings from different past efforts that explored the effects of minimum content on cultural heritage visitors. Minimum content was tested with visitors of different sites and lab experiment participants, focusing on augmented reality, games, and photography. The results reveal the potential of minimum content in cultural visits. The implications for HCI and future works are also discussed.