This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2554) Machine Learning (2076) Outbreak and Pandemic Preparedness and Management (1826) Artificial Intelligence (2129) Natural Language Processing (917)

Published on in Vol 24, No 11 (2022): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34067, first published .
Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

Predicting Emerging Themes in Rapidly Expanding COVID-19 Literature With Unsupervised Word Embeddings and Machine Learning: Evidence-Based Study

Authors of this article:

Ridam Pal1 Author Orcid Image ;   Harshita Chopra2 Author Orcid Image ;   Raghav Awasthi1 Author Orcid Image ;   Harsh Bandhey1 Author Orcid Image ;   Aditya Nagori1, 3 Author Orcid Image ;   Tavpritesh Sethi1 Author Orcid Image
Article Authors Cited by (3) Tweetations (6) Metrics

Journals

  1. García-Rudolph A, Sanchez-Pinsach D, Frey D, Opisso E, Cisek K, Kelleher J. Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus. Applied Sciences 2023;13(11):6713 View
  2. Aryani A, Wang J, Salvador-Carulla L, Woo J, Cheung C, Wu Z, Yin H, Xiao J, Lambert E, Howitt J, Davidson J, Yoong S, Dixon J, Climie R, Salinas-Perez J, Bagheri N, Santiago C, Williams J, Wickramasinghe N, Ng L, Zwack C, Lambert G. Coronavirus research topics, tracking twenty years of research. Scientific Data 2025;12(1) View

Conference Proceedings

  1. Kumar N, Mehrotra S. 2022 5th International Conference on Contemporary Computing and Informatics (IC3I). A Comparative Analysis of word embedding techniques and text similarity Measures View