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Everything about Topic model
Topical clustering method
In natural language processing, a topic model is a type of probabilistic, neural, or algebraic model for discovering the abstract topics that occur in a collection of documents. Topic modeling is a frequently used text mining tool for discovering hidden semantic features and structures in a text. The topics produced by topic models are generated through a variety of mathematical frameworks, including probabilistic generative models, matrix factorization methods based on word co-occurrence, and clustering algorithms applied to semantic embeddings. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Topic modelA 5-minute orientation
- Skim a recent paper: A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter PostsInfluential work since 2022
- Pick up a classic book: The Psychology of Everyday ThingsThe most-reprinted book on the subject
- Read the landmark paper: A correlated topic model of ScienceCited 933 times
- Go sideways: Pachinko allocationA closely related topic
- Go sideways: Latent Dirichlet allocationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
A correlated topic model of Science
ATM: Adversarial-neural Topic Model
LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
Topic Model for Graph Mining
Dynamic joint sentiment-topic model
Relational Biterm Topic Model: Short-Text Topic Modeling using Word Embeddings
Latent topic model for audio retrieval
Topic model validation
🆕 Influential since 2022
A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts
Semantic-enhanced topic evolution analysis: a combination of the dynamic topic model and word2vec
Cancer: A model topic for misinformation researchers
Hierarchical Interdisciplinary Topic Detection Model for Research Proposal Classification
Graph neural topic model with commonsense knowledge
📚 Books worth reading
The Psychology of Everyday ThingsDonald A. Norman · 1988
Basic econometricsDamodar N. Gujarati · 1978
Structural equation modeling with AMOSBarbara M Byrne, Barbara Byrne · 2000
Statistical methodsCharles Benedict Davenport · 1899- 📕Medical Product Safety EvaluationJie Chen, Joseph F. Heyse · 2018
Time seriesPeter J. Brockwell, Richard A. Davis · 1987
Six DegreesDuncan J. Watts · 2003
Structural equations with latent variablesKenneth A. Bollen · 1989
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Pachinko allocation In machine learning and natural language processing, the pachinko allocation model (PAM) is a topic model. Topic models are a suite of algorithms to uncover…
- Latent Dirichlet allocation In natural language processing, latent Dirichlet allocation (LDA) is a generative statistical model that explains how a collection of text documents can…
- Probabilistic latent semantic analysis Probabilistic latent semantic analysis (PLSA), also known as probabilistic latent semantic indexing (PLSI, especially in information retrieval circles)…
- Scott Deerwester Scott Craig Deerwester is an American computer scientist and computer engineer. He is known for his contributions to the development of latent semantic…
- Dynamic topic model Dynamic topic models are generative models in natural language processing that analyze the evolution of latent topics within a collection of documents…
- Distributional semantics Distributional semantics is a research area that develops and studies theories and methods for quantifying and categorizing semantic similarities between…
- Word embedding In natural language processing, a word embedding is a representation of a word. The embedding is used in text analysis. Typically, the representation is…
- Latent semantic analysis Latent semantic analysis (LSA) is a technique in natural language processing, in particular distributional semantics, of analyzing relationships between…