"A topic model is a type of statistical model for discovering the abstract 'topics' that occur in a collection of documents."
Discovering the underlying topics within a set of documents.
Natural Language Processing (NLP): The field of linguistics, computer science, and artificial intelligence that deals with the interaction between computers and human languages.
Statistical Learning: A modern approach to machine learning that uses statistical models and algorithms to predict the likelihood of future events based on past data.
Text Mining: The process of analyzing and extracting meaningful insights from unstructured text data.
Clustering: A technique used in unsupervised machine learning that groups similar objects together based on their attributes.
Latent Dirichlet Allocation (LDA): A popular topic modeling algorithm used to uncover hidden topics within a collection of documents.
Non-negative Matrix Factorization (NMF): Another popular topic modeling algorithm used to decompose a large data set into a set of smaller, more manageable components.
Feature Extraction: The process of reducing a dataset’s dimensionality by extracting the most relevant features.
Stopwords: Common words such as “the,” “and,” and “is” that are typically removed from text before analysis.
Stemming and Lemmatization: Techniques used to reduce words to their base forms for more accurate analysis.
Corpus and document representation: The process of converting raw text into a structured form that can be analyzed by a computer.
Topic coherence: A metric used to evaluate how coherent and interpretable the discovered topics are.
Big Data: Data sets that are too large and complex to be processed by traditional tools and techniques.
Sentiment Analysis: The process of using NLP techniques to analyze and evaluate the emotions and opinions expressed in text data.
Named Entity Recognition (NER): The process of identifying and categorizing information in text data that corresponds to entities such as people, businesses, and locations.
Deep Learning: A subset of machine learning that uses neural networks to model complex patterns in data.
Latent Dirichlet Allocation (LDA): LDA is one of the most popular models and is widely used in natural language processing for topic modeling. It is a generative statistical model that assumes the documents are generated from a mixture of topics, where each topic is a probability distribution over words. LDA tries to find the underlying topics that explain the observed documents.
Non-negative Matrix Factorization (NMF): NMF is another popular topic modeling technique that is based on matrix factorization. This model assumes that the data comes from a non-negative combination of topics and tries to find the underlying topics that explain the observed documents.
Probabilistic Latent Semantic Analysis (pLSA): PLSA is a statistical model that assumes the documents are generated from a mixture of topics, where each topic is a probability distribution over words. This model tries to find the underlying topics that explain the observed documents.
Hierarchical Dirichlet Process (HDP): HDP is a Bayesian nonparametric model that assumes the documents are generated from an infinite mixture of topics. This model tries to find the underlying topics that explain the observed documents.
Correlated Topic Model (CTM): CTM is a statistical model that assumes the documents are generated from a mixture of correlated topics, where each topic is a probability distribution over words. This model tries to find the underlying topics that explain the observed documents.
Author-Topic Model (ATM): ATM is an extension of LDA that allows for the modeling of authorship in addition to topics. This model assumes that each document is written by a specific author and tries to find the underlying topics and the authorship of the observed documents.
Dynamic Topic Model (DTM): DTM is a model that assumes the topics can change over time. This model tries to find the underlying topics and how they change over time in the observed documents.
Structural Topic Model (STM): STM is a model that assumes the topics have a structural relationship with covariates such as demographics, sentiment, or other metadata. This model tries to find the underlying topics and their structural relationship with other covariates in the observed documents.
"Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body."
"They also have applications in other fields such as bioinformatics and computer vision."
"Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies."
"A topic model captures this intuition in a mathematical framework, which allows examining a set of documents and discovering, based on the statistics of the words in each, what the topics might be and what each document's balance of topics is."
"Intuitively, given that a document is about a particular topic, one would expect particular words to appear in the document more or less frequently."
"A document typically concerns multiple topics in different proportions; thus, in a document that is 10% about cats and 90% about dogs, there would probably be about 9 times more dog words than cat words."
"The 'topics' produced by topic modeling techniques are clusters of similar words."
"Topic models have been used to detect instructive structures in data such as genetic information."
"Topic models have been used to detect instructive structures in data such as... images."
"Topic models can help to organize and offer insights for us to understand large collections of unstructured text bodies."
"Topic models are also referred to as probabilistic topic models."
"In the age of information, the amount of the written material we encounter each day is simply beyond our processing capacity."
"[Probabilistic topic models are] statistical algorithms for discovering the latent semantic structures of an extensive text body."
"[Topic models] allows examining a set of documents and discovering... what the topics might be and what each document's balance of topics is."
"Topic modeling is a frequently used text-mining tool for the discovery of hidden semantic structures in a text body."
"Originally developed as a text-mining tool..."
"Topic models have been used to detect instructive structures in data such as... networks."
"'The' and 'is' will appear approximately equally in both [dog and cat topics]."
"Topic models have been used to detect instructive structures in data such as... genetic information, images, and networks... bioinformatics."