LibGuides: Text Analysis: Topic Modeling

Overview - Topic Modeling

Topic modeling discovers underlying themes or topics (groups of co-occurring words) across a large set of texts.

The Evolution of Topic Modeling
Churchill, Rob, and Lisa Singh. “The Evolution of Topic Modeling.” ACM computing surveys 54.10s (2022): 1–35.
Topic Modeling - Types, Working, Applications
“Topic Modeling - Types, Working, Applications.” GeeksforGeeks, 13 June 2024, www.geeksforgeeks.org/what-is-topic-modeling/.
Topic Modeling: Text Mining with R
Robinson, Julia Silge and David. “6 Topic Modeling: Text Mining with R.” A Tidy Approach, www.tidytextmining.com/topicmodeling.html.
Topic Modeling Workshop (SICSS)
Topic Modeling, cbail.github.io/SICSS_Topic_Modeling.html.
An Overview of Topic Modeling Methods and Tools
Barde, Bhagyashree Vyankatrao, and Anant Madhavrao Bainwad. "An overview of topic modeling methods and tools." 2017 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2017.
Reading Tea Leaves: How Humans Interpret Topic Models
Chang, Jonathan, et al. "Reading tea leaves: How humans interpret topic models." Advances in neural information processing systems 22 (2009). papers.neurips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models.pdf.
Co‐word maps and topic modeling
Leydesdorff, Loet, and Adina Nerghes. "Co‐word maps and topic modeling: A comparison using small and medium‐sized corpora (N< 1,000)." Journal of the Association for Information Science and Technology 68.4 (2017): 1024-1035.

jsLDA
An in-browser interactive topic modeling tool for experimenting with Latent Dirichlet Allocation (LDA).
NLTK (Natural Language Toolkit)
A comprehensive suite of Python libraries for text processing, including classification, tokenization, and parsing.
Gensim
A robust Python library for topic modeling and document similarity analysis using algorithms like LDA (latent Dirichlet allocation).
tidytext
R package to manipulate and analyze text data using tidy data principles.
text2vec
High-performance tools for text vectorization, similarity, and classification in R.
topicmodels
R package that implements algorithms like LDA (latent Dirichlet allocation) for discovering latent topics in text collections.
lda (Latent Dirichlet Allocation)
R package that provides Gibbs sampling implementations of LDA (latent Dirichlet allocation) for topic modeling.
BTM (Biterm Topic Modeling)
An R package for Biterm Topic Modeling, especially suited for short texts like tweets.
stm (Structural Topic Model)
R package that fits topic models that incorporate document-level metadata to influence topic prevalence or content.
quanteda
A fast, flexible, and comprehensive framework for quantitative text analysis in R.