Topic modeling
Topic modeling is an unsupervised statistical or computational technique for inferring recurring latent thematic structures from a collection of documents.
Starting Data Analytics with Generative AI and Python
Topic modeling is a statistical or machine-learning technique that discovers recurring abstract topics in a collection of documents.
- How it is used
- Identifies common concerns across collections such as negative customer reviews when individual summaries would remain cumbersome to consolidate.
- How deep it goes
- Explains document-topic mixtures and word distributions, then demonstrates preprocessing, filtering, output interpretation, and translation.
- Examples
- Uses Portuguese negative customer reviews, five extracted topics, English translations, and interpretations about delivery, products, purchases, and missing items.
- What you can do after
- Apply it via Python, pandas, NLTK, and generative AI to extract and interpret recurring concerns in filtered review collections.
Read how Starting Data Analytics with Generative AI and Python explains topic modeling →
Outlier Detection in Python
Topic modeling is an unsupervised technique for inferring recurring semantic topics and their distributions in a collection of documents.
- How it is used
- Uses document content to create topic features for outlier detection, interpretation, and text clustering.
- How deep it goes
- Explains co-occurring words, document-topic scores, interpretation limits, and practical tooling without deriving the underlying algorithms.
- Examples
- Demonstrates expense-comment topics and BERTopic assignments on the 20 Newsgroups dataset.
- What you can do after
- Apply it via BERTopic to generate topics, document assignments, and topic probabilities for text collections.
Read how Outlier Detection in Python explains topic modeling →
Related concepts
- latent Dirichlet allocationimplemented by · Latent Dirichlet allocation is a generative probabilistic topic model representing documents as mixtures of latent topics and topics as distributions over words.
- summarizationcontrasts with · Summarization is the process of condensing source content into a shorter representation that preserves its important information or meaning.
- textuses · Text is written or digitally encoded linguistic content represented as characters, words, tokens, or other textual units.
- topiccontrasts with · A topic is a named messaging destination or stream to which producers publish messages or records and from which consumers receive or read them.
- dimensionality reductionimplements · Dimensionality reduction is the transformation of data into a representation with fewer dimensions while retaining selected information, structure, or relationships.
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