Topic model
1.1. What is Topic Modeling? - Introduction to Python for Humanists
Topic modeling is an approach in NLP where we try to find hidden themes within a collection of documents in a corpus. These themes are the topics. Topic ...
(PDF) Topic Modeling: A Comprehensive Review - ResearchGate
PDF | Topic modelling is the new revolution in text mining. It is a statistical technique for revealing the underlying semantic structure in ...
The Structural Topic Model is a general framework for topic modeling with document-level covariate information.
Probabilistic topic models - CS@Columbia
Topic modeling algorithms do not require any prior annotations or labeling of the documents—the topics emerge from the analysis of the origi- nal texts. Topic ...
Insight Series: What is a Topic Model? - Metia
Topic modelling is a technique used to extract a list of meaningful topics that appear within a large dataset. In a typical client project, ...
Topic Modeling: Algorithms, Techniques, and Application
Topic Modeling is treated as a form of tagging and primarily used for information retrieval wherein it helps in query expansion.
Topic Modeling - Alteryx Help Documentation
Configure the Tool · Add a Topic Modeling tool to the canvas. · Use the anchor to connect the Topic Modeling tool to the text data you want to use in the ...
Topic Modeling Explained: LDA to Bayesian Inference
Topic modeling offers a way to compress this data while preserving the statistical relationships crucial for ranking relevant results.
Topic Modelling in Natural Language Processing - Analytics Vidhya
A. Topic modeling is a natural language processing technique that uncovers latent topics within a collection of text documents. It helps ...
Topic Modeling in Embedding Spaces - ACL Anthology
More specifically, the etm models each word with a categorical distribution whose natural parameter is the inner product between the word's embedding and an ...
replicability / reproducibility in topic modeling (LDA)
Topic modeling (LDA) is not replicable, ie it gives different results in different runs. Where does this come from (where does this randomness come from and ...
Description Provides an interface to the C code for Latent Dirichlet. Allocation (LDA) models and Correlated Topics Models. (CTM) by David M. Blei and co- ...
Fast and Scalable Algorithms for Topic Modeling
We propose a fast sampling algorithm F+LDA, where T is the number of topics. F+LDA only costs O(log T) times by utilizing the F+tree data structure.
Building a Topic Model: What Is it, Why, and How? - DMNews
What is topic modeling? Topic modeling identifies repeated phrases and contextual cues to find common themes or categories of content in a collection of long- ...
Interactive topic modeling | Machine Learning
This framework, interactive topic modeling (itm), allows untrained users to encode their feedback easily and iteratively into the topic models. Because latency ...
Topic Models: Introduction - YouTube
This is a single lecture from a course. If you you like the material and want more context (e.g., the lectures that came before), ...
Smart literature review: a practical topic modelling approach to ...
This paper presents a framework where topic modelling, a branch of the unsupervised methods, is used to conduct an exploratory literature review.
LDA topic modeling - Training and testing - Stack Overflow
You train the model (like LDA) on the training set, and then you see how "perplexed" the model is on the testing set. More specifically, you ...
The Structural Topic Model and Applied Social Science∗
Over the last decade probabilistic topic models, such as Latent Dirichlet Allocation (LDA), have become a common tool for understanding large text corpora [1].1 ...
Using LDA Topic Models as a Classification Model Input
The idea here is to test whether the distribution per review of hidden semantic information could predict positive and negative sentiment.