No abstract

Recent efforts have focused on identifying multidisciplinary teams and detecting co-Authorship Networks based on exploring topic modeling to identify researchers’ expertise. Though promising, none of these efforts perform a real-life evaluation of the quality of the built topics. This paper proposes a Semantic Academic Profiler ( SAP ) framework that allows summarizing articles written by researchers to automatically build research profiles and perform online evaluations regarding these built profiles. SAP exploits and extends state-of-the-art Topic Modeling strategies based on Cluwords considering n-grams and introduces a new visual interface able to highlight the main topics related to articles, researchers and institutions. To evaluate SAP’s capability of summarizing the profile of such entities as well as its usefulness for supporting online assessments of the topics’ quality, we perform and contrast two types of evaluation, considering an extensive repository of Brazilian curricula vitae: (1) an offline evaluation, in which we exploit a traditional metric (NPMI) to measure the quality of several data representations strategies including (i) TFIDF, (ii) TFIDF with Bi-grams, (iii) Cluwords, and (iv) CluWords with Bi-grams; and (2) an online evaluation through an A/B test where researchers evaluate their own built profiles. We also perform an online assessment of SAP user interface through a usability test following the SUS methodology. Our experiments indicate that the CluWords with Bi-grams is the best solution and the SAP interface is very useful. We also observed essential differences in the online and offline assessments, indicating that using both together is very important for a comprehensive quality evaluation. Such type of study is scarce in the literature and our findings open space for new lines of investigation in the Topic Modeling area.

In this work, we present the platform Boca a Boca Virtual, a kind of virtual catalog that aims to help self-employed professionals and small businesses to start their presence in the digital environment, promoting their services and products in a simple and fast way. The idea emerged during the beginning of the COVID-19 pandemic, especially with social isolation, whose concern, from an economic and social point of view, was to allow autonomous service providers and establishments, in general, to continue offering their products and/or services. Through the platform, a consumer can search for a specific product and service within his own neighborhood and city, strengthening the local economy. In addition, Boca a Boca Virtual is integrated with Google Maps, in such a way that registered developments are also indexed by the Google search engine, improving their visibility and potentially increasing more customers. The platform is fully responsive, combining several recent web development technologies. Today it has more than 140 registered enterprises, from more than 30 Brazilian cities.

No abstract

In this work, we propose a framework that automatically extracts semantic topics from scientific publications related to research on COVID-19. The framework has four main building blocks: (i) preprocessing, (ii) topic modeling, (iii) topic correlation with authors and institutions, and (iv) summarization interface. The first block corresponds to the application of pre-processing strategies in texts on the considered articles and the definition of their semantic representation. The topic modeling block aims to fi nd semantic topics in the publications used. The third block correlates these topics with the articles themselves and the authors, institutions, and countries related to each article. The summary interface provides an intuitive view for all these analyses. Our evaluation shows that our framework is capable of automatically extracting relevant characteristics from the articles, identifying the main themes addressed by them, as well as the correlation of researchers, institutions and countries for diff erent topics of research on COVID-19.