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Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models similar to Latent Dirichlet Allocation (LDA) or neural topic…
Text summarization is an approach for identifying important information present within text documents. This computational technique aims to generate shorter versions of the source text, by including only the relevant and salient information…
Text-rich heterogeneous information networks (text-rich HINs) are ubiquitous in real-world applications. Hypernymy, also known as is-a relation or subclass-of relation, lays in the core of many knowledge graphs and benefits many downstream…
Topic models provide a useful text-mining tool for learning, extracting, and discovering latent structures in large text corpora. Although a plethora of methods have been proposed for topic modeling, lacking in the literature is a formal…
Community detection is one of the most active fields in complex networks analysis, due to its potential value in practical applications. Many works inspired by different paradigms are devoted to the development of algorithmic solutions…
Natural language processing tools have become frequently used in social sciences such as economics, political science, and sociology. Many publications apply topic modeling to elicit latent topics in text corpora and their development over…
A particular challenge in the area of social media analysis is how to find communities within a larger network of social interactions. Here a community may be a group of microblogging users who post content on a coherent topic, or who are…
Topic models are a popular tool for clustering and analyzing textual data. They allow texts to be classified on the basis of their affiliation to the previously calculated topics. Despite their widespread use in research and application, an…
Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our…
Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can potentially discover a broad range of themes in a data set,…
Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea.…
In recent years, social networks have shown diversity in function and applications. People begin to use multiple online social networks simultaneously for different demands. The ability to uncover a user's latent topic and social network…
Social media platforms host discussions about a wide variety of topics that arise everyday. Making sense of all the content and organising it into categories is an arduous task. A common way to deal with this issue is relying on topic…
The rise of a trending topic on Twitter or Facebook leads to the temporal emergence of a set of users currently interested in that topic. Given the temporary nature of the links between these users, being able to dynamically identify…
Dependency analysis is recognized as an important field of software engineering due to a variety of reasons. There exists a large pool of tools providing assistance to software developers and architects. Analysis of inter- and intra-project…
Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process…
We have developed a set of Python applications that use large language models to identify and analyze data from social media platforms relevant to a population of interest. Our pipeline begins with using OpenAI's GPT-3 to generate potential…
Many of quality approaches are described in hundreds of textual pages. Manual processing of information consumes plenty of resources. In this report we present a text mining approach applied on CMMI, one well known and widely known quality…
Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and…
Twitter is increasingly used for political, advertising and marketing campaigns, where the main aim is to influence users to support specific causes, individuals or groups. We propose a novel methodology for mining and analyzing Twitter…