7 citations · 7 across the 3 of their papers we have counts for
8 papers
Identifying epidemic related Tweets using noisy learning
Ramya Tekumalla, Juan M. Banda
Supervised learning algorithms are heavily reliant on annotated datasets to train machine learning models. However, the curation of the annotated datasets is laborious and time con…
A Biomedically oriented automatically annotated Twitter COVID-19 Dataset
Luis Alberto Robles Hernandez, Tiffany J. Callahan, Juan M. Banda
The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontra…
Characterizing drug mentions in COVID-19 Twitter Chatter
Ramya Tekumalla, Juan M. Banda
Since the classification of COVID-19 as a global pandemic, there have been many attempts to treat and contain the virus. Although there is no specific antiviral treatment recommend…
GLEAKE: Global and Local Embedding Automatic Keyphrase Extraction
Javad Rafiei Asl, Juan M. Banda
Automated methods for granular categorization of large corpora of text documents have become increasingly more important with the rate scientific, news, medical, and web documents…
A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
Ramya Tekumalla, Juan M. Banda
With the increase in popularity of deep learning models for natural language processing (NLP) tasks, in the field of Pharmacovigilance, more specifically for the identification of…
Social Media Mining Toolkit (SMMT)
Ramya Tekumalla, Juan M. Banda
There has been a dramatic increase in the popularity of utilizing social media data for research purposes within the biomedical community. In PubMed alone, there have been nearly 2…