Search results

The Islamic world spans many different languages with different language structures. This paper aims to explore one way in which language structure affects consumer response to the marketing of cobrands.

Two between subject experiments were conducted using samples of participants from Saudi Arabia and the USA. The first manipulated partner brand category similarity and brand name order, along with the structure of the language used to communicate with the market. The data for this study includes Arabic speakers in Saudi Arabia as well as English speakers in the USA. The second study explores how targeting a population fluent in multiple languages of varied structure nullifies the findings from the first study and uses Latino participants in the USA.

This study finds that when brands come from similar product categories, name order did not affect cobrand evaluations, but it did when the brands come from dissimilar product categories. Here, evaluations of the cobrand are enhanced when the invited brand is in the position that adjectives occupy in the participant’s language. The authors also find that being proficient in two languages, each with a different default order for adjectives and nouns, quashes the effect of name order otherwise seen when brands from dissimilar product categories engage in cobranding.

By examining the impact of language structure on the effects of cobrand evaluation and conducting studies among participants with differing dominant languages, this research can rule out simple primacy or recency effects.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research.

A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens.

Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation.

Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy.

Dictionary training, aligned with information literacy training, can be informed by this paper.

The value of dictionary literacy for all sectors of societies can be improved.

Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.

The purpose of this study is to develop a domain independent, cost-effective, time-saving and semi-automated ontology generation framework that could extract taxonomic concepts from unstructured text corpus. In the human disease domain, ontologies are found to be extremely useful for managing the diversity of technical expressions in favour of information retrieval objectives. The boundaries of these domains are expanding so fast that it is essential to continuously develop new ontologies or upgrade available ones.

This paper proposes a semi-automated approach that extracts entities/relations via text mining of scientific publications. Text mining-based ontology (TmbOnt)-named code is generated to assist a user in capturing, processing and establishing ontology elements. This code takes a pile of unstructured text files as input and projects them into high-valued entities or relations as output. As a semi-automated approach, a user supervises the process, filters meaningful predecessor/successor phrases and finalizes the demanded ontology-taxonomy. To verify the practical capabilities of the scheme, a case study was performed to drive glaucoma ontology-taxonomy. For this purpose, text files containing 10,000 records were collected from PubMed.

The proposed approach processed over 3.8 million tokenized terms of those records and yielded the resultant glaucoma ontology-taxonomy. Compared with two famous disease ontologies, TmbOnt-driven taxonomy demonstrated a 60%–100% coverage ratio against famous medical thesauruses and ontology taxonomies, such as Human Disease Ontology, Medical Subject Headings and National Cancer Institute Thesaurus, with an average of 70% additional terms recommended for ontology development.

According to the literature, the proposed scheme demonstrated novel capability in expanding the ontology-taxonomy structure with a semi-automated text mining approach, aiming for future fully-automated approaches.

The purpose of the paper is to present an integrated methodology for identifying trends in a particular subject area based on a combination of advanced text mining and expert methods. The authors aim to test it in an area of clinical psychology and psychotherapy in 2010–2019.

The authors demonstrate the way of applying text-mining and the Word2Vec model to identify hot topics (HT) and emerging trends (ET) in clinical psychology and psychotherapy. The analysis of 11.3 million scientific publications in the Microsoft Academic Graph database revealed the most rapidly growing clinical psychology and psychotherapy terms – those with the largest increase in the number of publications reflecting real or potential trends.

The proposed approach allows one to identify HT and ET for the six thematic clusters related to mental disorders, symptoms, pharmacology, psychotherapy, treatment techniques and important psychological skills.

The developed methodology allows one to see the broad picture of the most dynamic research areas in the field of clinical psychology and psychotherapy in 2010–2019. For clinicians, who are often overwhelmed by practical work, this map of the current research can help identify the areas worthy of further attention to improve the effectiveness of their clinical work. This methodology might be applied for the identification of trends in any other subject area by taking into account its specificity.

The paper demonstrates the value of the advanced text-mining approach for understanding trends in a subject area. To the best of the authors’ knowledge, for the first time, text-mining and the Word2Vec model have been applied to identifying trends in the field of clinical psychology and psychotherapy.

This study aims to explore the emotional landscapes and spatial preferences of digital nomads, focusing on how sentiments expressed in destination reviews influence their mobility and destination choices.

Employing a lexicon-based sentiment analysis of social media comments and reviews, alongside advanced geographical information systems (GIS) mapping techniques, the study analyzes the emotional tones that digital nomads associate with various destinations worldwide.

The analysis reveals significant patterns of emotional sentiments, with trust and joy being predominant in preferred destinations. Spatial patterns identified through GIS mapping highlight the global distribution of these sentiments, underscoring the importance of emotional well-being in destination choice.

Insights from this study offer valuable guidance for Destination Management Organizations (DMOs) in strategic planning, enhancing destination appeal through targeted marketing strategies that resonate with the emotional preferences of digital nomads.

This research introduces a novel approach by integrating sentiment analysis with GIS to map the emotional and spatial dynamics of digital nomadism, contributing a new perspective to the literature on tourism and mobility.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

Celebrate Michael Buckland's impressive legacy to LIS by showing his humanity, generosity and versatility.

This article is walk through a scientific career in LIS. Through personal anecdotes and life history and building upon Michael Buckland's legacy, it summarises the author’s own work seen through the prism of her interactions with Buckland, leading to scholarly contributions articulating significant statements about the field of LIS as well as pointers to past relevant publications.

Michael Buckland has a unique way of putting an end to thorny LIS issues as well as being a documentator extraordinaire.

It is a personal account, as such cannot be evaluated through the classical norms of empirical research as there is no ground truth. This account shows how chance encounters with fellow scholars can have a lasting influence on one's academic career as well as wider impact in a field.