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[PDF][PDF] Topic modeling: a comprehensive review.
accepted on 03 July 2019, published on 24 July 2019 Copyright© 2019 Pooja Kherwa et al.,
licensed to EAI. This is an open access article distributed under the terms of the Creative …
licensed to EAI. This is an open access article distributed under the terms of the Creative …
An overview of topic modeling and its current applications in bioinformatics
L Liu, L Tang, W Dong, S Yao, W Zhou - SpringerPlus, 2016 - Springer
Background With the rapid accumulation of biological datasets, machine learning methods
designed to automate data analysis are urgently needed. In recent years, so-called topic …
designed to automate data analysis are urgently needed. In recent years, so-called topic …
Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey
Topic modeling is one of the most powerful techniques in text mining for data mining, latent
data discovery, and finding relationships among data and text documents. Researchers …
data discovery, and finding relationships among data and text documents. Researchers …
An integrated clustering and BERT framework for improved topic modeling
L George, P Sumathy - International Journal of Information Technology, 2023 - Springer
Topic modelling is a machine learning technique that is extensively used in Natural
Language Processing (NLP) applications to infer topics within unstructured textual data …
Language Processing (NLP) applications to infer topics within unstructured textual data …
A model of text for experimentation in the social sciences
ME Roberts, BM Stewart, EM Airoldi - Journal of the American …, 2016 - Taylor & Francis
Statistical models of text have become increasingly popular in statistics and computer
science as a method of exploring large document collections. Social scientists often want to …
science as a method of exploring large document collections. Social scientists often want to …
[PDF][PDF] Baselines and bigrams: Simple, good sentiment and topic classification
SI Wang, CD Manning - Proceedings of the 50th Annual Meeting …, 2012 - aclanthology.org
Abstract Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used
as baseline methods for text classification, but their performance varies greatly depending …
as baseline methods for text classification, but their performance varies greatly depending …
Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis
BAH Murshed, S Mallappa, J Abawajy… - Artificial Intelligence …, 2023 - Springer
Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly
embraced by individuals, groups, and organizations as a valuable source of information …
embraced by individuals, groups, and organizations as a valuable source of information …
Topical word embeddings
Most word embedding models typically represent each word using a single vector, which
makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to …
makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to …
[PDF][PDF] Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora
D Ramage, D Hall, R Nallapati… - Proceedings of the 2009 …, 2009 - aclanthology.org
A significant portion of the world's text is tagged by readers on social bookmarking websites.
Credit attribution is an inherent problem in these corpora because most pages have multiple …
Credit attribution is an inherent problem in these corpora because most pages have multiple …
Improving topic models with latent feature word representations
DQ Nguyen, R Billingsley, L Du… - Transactions of the …, 2015 - direct.mit.edu
Probabilistic topic models are widely used to discover latent topics in document collections,
while latent feature vector representations of words have been used to obtain high …
while latent feature vector representations of words have been used to obtain high …