LDA (Latent Dirichlet Allocation) is a powerful technique used for topic modeling. It teaches us that every document is comprised of several different topics, and each topic consists of similar words.

This project uses unsupervised learning to group reddit text and identify major conspiracy theories using NLP, LDA, spacy, SVD, SBert embedding and HDSCAN. Spacy model "en_core_web_sm" has been used ...

Abstract: Technological innovation in today's society has led to a highly interconnected world, the fruit of which has been an unprecedented breakthrough in new technologies and a tremendous growth of ...

Abstract: This paper proposes a model for the topic modelling of tweets in the health and mental health domain using the Latent Dirichlet Allocation (LDA) method. The data were obtained from the ...

Topic modelling is a text mining technique utilised to discover patterns in textual data. Given a large collection of text documents, a topic model can extract topics that best represent the given ...

I was genuinely excited to dive into the world of topic modelling tools. After realising I needed a way to generate topic models from a range of text attributes, I set out to explore several popular ...