This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

Sentiment analysis has become an important task in natural language processing because it
is used in many different areas. This paper gives a detailed review of sentiment analysis …

Objective Social media-based public health research is crucial for epidemic surveillance, but
most studies identify relevant corpora with keyword-matching. This study develops a system …

Background: The COVID-19 pandemic has caused a disproportionate impact on the sexual
and gender-diverse (SGD) community. Compared with non-SGD populations, their social …

Accurately assigning standardized diagnosis and procedure codes from clinical text is
crucial for healthcare applications. However, this remains challenging due to the complexity …

The task of extracting and classifying entities is at the core of important Health-NLP systems
such as misinformation detection, medical dialogue modeling, and patient-centric …

Objective Harnessing drug-related data posted on social media in real time can offer
insights into how the pandemic impacts drug use and monitor misinformation. This study …

With the outbreak of the epidemic, it has had a major impact on the economy, society, and
people's lives. The entity mining of network public opinion is important, which is helpful for …

The restricted access to data in healthcare facilities due to patient privacy and confidentiality
policies has led to the application of general natural language processing (NLP) techniques …

Objective Current studies leveraging social media data for disease monitoring face
challenges like noisy colloquial language and insufficient tracking of user disease …