In the rapidly evolving field of natural language processing, the demand for efficient
automated text summarization systems that not only distill extensive documents but also …

Topic modeling has become a popular approach to identify semantic structures in text
corpora. Despite its wide applications, interpreting the outputs of topic models remains …

The clinical notes in electronic health records have many possibilities for predictive tasks in
text classification. The interpretability of these classification models for the clinical domain is …

TextNetTopics (Yousef et al. in Front Genet 13: 893378, 2022. https://doi. org/10.3389/fgene.
2022.893378) is a recently developed approach that performs text classification-based …

Topic modeling is a prevalent task for discovering the latent structure of a corpus, identifying
a set of topics that represent the underlying themes of the documents. Despite its popularity …

Game reviews function as an important customercreated resource for game studies as they
allow practitioners and developers to analyse players' opinions. Despite this, there are few …

Unstructured text data is collected daily in large amounts by many organizations. Analyzing
all this data is time intensive and too costly in many cases. One technique to systematically …

Document classification is a widely used task for analyzing mental healthcare texts. This
systematic literature review focuses on the document classification of electronic health …

Social media stands as a crucial information source across various real-world challenges.
Platforms like Twitter, extensively used by news outlets for real-time updates, categorize …

The written notes in the Electronic Health Records contain a vast amount of information
about patients. Implementing automated approaches for text classification tasks requires the …