clarallelogram / TopicModeling

Unsupervised Topic Modelling project using Latent Dirichlet Allocation (LDA) on the NeurIPS papers. Built as part of the final project for McGill AI Society's Accelerated Introduction to Machine Learning Course (MAIS 202).

MAIS-202-Final-Project: Topic Modeling Using LDA

Theory Notes:

This is a file that contains (messy) notes on the theory behind topic modeling and LDA

References:

“Introduction to Latent Dirichlet Allocation.” Edwin Chens Blog Atom, https://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/.

“Holistic Sentiment Analysis across Languages, Part I: LDA.” N. Saphra Misunderstands NLP Papers, http://confusedlanguagetech.blogspot.com/2012/07/jordan-boyd-graber-and-philip-resnik.html.

Kapadia, Shashank. “Topic Modeling in Python: Latent Dirichlet Allocation (LDA).” Medium, Towards Data Science, 23 Dec. 2022, https://towardsdatascience.com/end-to-end-topic-modeling-in-python-latent-dirichlet-allocation-lda-35ce4ed6b3e0.

Rajeev, Malvika. “Using LDA to Find Trends in ML Papers.” Malvika R, Malvika R, 7 Sept. 2020, https://www.malvikarajeev.com/post/lda/.

Jelodar, Hamed, et al. “Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey.” ArXiv.org, 6 Dec. 2018, https://arxiv.org/abs/1711.04305.