Topic model
A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.
What Is Topic Modeling? A Beginner's Guide
Topic modeling defined. Topic modeling is a type of statistical modeling that uses unsupervised Machine Learning to identify clusters or groups ...
Topic models are an unsupervised NLP method for summarizing text data through word groups. They assist in text classification and ...
Topic Modelling - Text Analysis - Guides at Penn Libraries
What is Topic Modeling? Topic modeling is a type of statistical modeling used to identify topics or themes within a collection of documents. It ...
What is Topic Modeling? An Introduction With Examples - DataCamp
Topic modeling is a frequently used approach to discover hidden semantic patterns portrayed by a text corpus and automatically identify topics that exist ...
Making sense of topic models - Medium
Topic models can find useful exploratory patterns, but they're unable to reliably capture context or nuance. They cannot assess how topics ...
Topic Modeling: Definition, Benefits and Use Cases - Qualtrics
Topic modeling is an unsupervised statistical method for discovering abstract 'topics' that exist within a collection of documents.
Topic modeling basics - Conversational Insights - Google Cloud
When you use topic modeling to analyze conversations, Insights creates a topic model. Topic models contain discovered topics and can be used to infer topics for ...
Topic Modeling: Research Tutorials - Indiana University
Topic modeling is an algorithm-based tool that identifies the co-occurrence of words in a large document set.
Topic Models | Papers With Code
A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently ...
6 Topic modeling - Text Mining with R
Topic modeling is a method for unsupervised classification of such documents, similar to clustering on numeric data, which finds natural groups of items.
Most topic models, such as latent Dirichlet allocation (LDA) [4], are unsupervised: only the words in the documents are modelled. The goal is to infer topics ...
(NLP) Best practices for topic modeling and generating interesting ...
All I've really seen is leveraging BERT, umap, and hdbscan to form topics based on semantic similarity. All of these methods require a ton of human fine-tuning.
Topic modeling overview | Conversational Insights Documentation
Topic modeling overview ... Topic modeling helps you discover topics (call drivers) in conversations between contact center agents and end users. These ...
An intro to topic models for text analysis | by Patrick van Kessel
A topic model is a type of algorithm that scans a set of documents (known in the NLP field as a corpus), examines how words and phrases co-occur ...
Topic Modeling for the People - Maria Antoniak
In this quick and practical guide, I'm going to share a set of steps that you can follow to get coherent topics from most datasets.
[2401.15351] A Survey on Neural Topic Models - arXiv
In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges. Specifically, we ...
[Discussion] Are there any better Topic Modelling algorithms/models ...
For topic modeling, LDA and NMF (non-negative Matrix Factorization) are popular algorithms. LDA is probabilistic model and NMF is a multivariate ...
Dynamic Topic Models - David Mimno
First, we review the underlying statistical assumptions of a static topic model, such as latent Dirichlet allocation. (LDA) (Blei et al., 2003). Let β1:K be K ...
Topic Modeling - Types, Working, Applications - GeeksforGeeks
Topic modelling is a system learning technique that robotically discovers the principle themes or “topics” that represents a huge collection of documents.