"A topic model is a type of statistical model for discovering the abstract 'topics' that occur in a collection of documents."
Discovering the underlying themes or topics in a collection of documents.
Natural Language Processing (NLP): This is a field that deals with the interaction between computers and human (natural) languages. It includes techniques such as tokenization, stemming, and part-of-speech tagging, among others.
Corpus: A corpus is a collection of texts that are used for analysis. It can be a collection of books, articles, or any other form of written material. It can be obtained from public sources, such as Wikipedia or news articles, or generated for specific domains, such as medical or legal texts.
Text Preprocessing: This involves preparing text data for analysis. It can include removing stop words, stemming or lemmatizing words, and converting text to lowercase or removing punctuation marks.
Vector Space Model: This is a mathematical model that represents each text document or word as a vector of numeric features, such as a bag-of-words representation or a TF-IDF score.
Latent Semantic Analysis (LSA): This is a method that uses vector space models to identify patterns in text data. It can be used to find underlying patterns in large sets of unstructured data or to categorize documents based on their content.
Latent Dirichlet Allocation (LDA): This is a probabilistic topic modeling technique that assumes that each document is a mixture of topics and each word in the document is generated from one of those topics.
Gibbs Sampling: This is a Markov Chain Monte Carlo method that is used to estimate the posterior distribution of a target variable. It is often used to sample from the distribution of topics in LDA.
Topic Coherence: This measures the semantic coherence of a set of topics or documents. It is often used to evaluate the quality of topic models.
Topic Modeling Evaluation Metrics: These are measures used to evaluate the quality of topic models. Some popular metrics include perplexity, coherence, and document classification accuracy.
Topic Modeling Applications: These are areas where topic modeling is used, such as sentiment analysis, content recommendation, and document categorization.
Latent Semantic Analysis (LSA): It uses singular value decomposition to identify the relationships between words in a document and create a matrix representation of the document corpus.
Latent Dirichlet Allocation (LDA): It's a generative probabilistic model that assumes that each document in the corpus is generated from a mixture of different topics, and each topic is a distribution of words.
Non-negative Matrix Factorization (NMF): It's a matrix decomposition technique that approximates a matrix into two non-negative matrices representing a set of underlying topics and their corresponding proportions within each document.
Hierarchical Dirichlet Process (HDP): It's a Bayesian non-parametric extension to LDA that allows for an infinite number of topics and automatically discovers the optimal number of topics.
Correlated Topic Model (CTM): It's an extension of LDA that models the correlation between topics and improves the performance of topic modeling on short texts.
Gibbs Sampling for Latent Gaussian Models (GSLGM): It's a hierarchical model that assumes each document is generated from a multinomial distribution over topics, and each topic is generated from a multivariate Gaussian distribution.
Symmetric Non-negative Matrix Factorization (Sym-NMF): It's an extension of NMF that allows for the discovery of overlapping clusters, which are used to represent multiple topics in a single document.
Aspect-Based Topic Modeling (ABTM): It's a technique that identifies aspects or features of a product or service and discovers the topics that are related to those aspects.
Structural Topic Modeling (STM): It's a technique that models the relationships between topics, words, and metadata such as author or publication date to generate more accurate and interpretable topic models.
Bayesian Additive Regression Trees for Topic Modeling (BART): It's a non-parametric regression method that models the relationship between the topics and the predictors to improve the accuracy of topic modeling.
"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."