Wikipedia
Topic Modeling

A statistical model for discovering the abstract "topics" that occur in a collection of documents.

27 Documents (Long List)

- Semantic hashing using tags and topic modeling (2013)
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Semantic Hashing using Tags and Topic Modeling, to incorporate both the tag information and the similarity information from probabilistic topic modeling. [Comments about the paper](https://sutheeblog.wordpress.com/2016/10/28/paper-reading-semantic-hashing-using-tags-and-topic-modeling-sigir13/). [Code on Github](https://github.com/zhuoxiongzhao/code-for-SHTTM)

2018-03-22 - Topic Modeling with Scikit Learn – Aneesha Bakharia – Medium
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2017-12-05 - DS Toolbox - Topic Models - DS lore
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Usefulness of topic models and word embeddings for non-NLP tasks

2017-11-21 - News classification with topic models in gensim
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2017-06-07 - pyLDAvis
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Python library for interactive topic model visualization. Designed to help users interpret the topics.

see also another notebook dedicated to using it with gensim (include nltk_stopwords,...)

2017-06-02 - Stanford Topic Modeling Toolbox
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2017-05-23 - Topic modeling made just simple enough. | The Stone and the Shell
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2017-05-22 - Topic Modeling for Humanists: A Guided Tour
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2017-05-19 - Topic Modeling in the Humanities: An Overview - Maryland Institute for Technology in the Humanities
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2017-05-19 - Using Word2Vec for topic modeling - Stack Overflow
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2017-05-19 - Sapping Attention: When you have a MALLET, everything looks like a nail
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2014-04-25 - machine learning - Unsupervised automatic tagging algorithms? - Stack Overflow
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2014-04-24 - Topic Modeling and Network Analysis | the scottbot irregular
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Great post

2014-04-23 - Provable Algorithms for Machine Learning Problems by Rong Ge.
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from the abstract:

Modern machine learning algorithms can extract useful information from text, images and videos. All these applications involve solving NP-hard problems in average case using heuristics. What properties of the input allow it to be solved effciently? Theoretically analyzing the heuristics is very challenging. Few results were known.

This thesis takes a different approach: we identify natural properties of the input, then design new algorithms that provably works assuming the input has these properties. We are able to give new, provable and sometimes practical algorithms for learning tasks related to text corpus, images and social networks.

...In theory, the assumptions in this thesis help us understand why intractable problems in machine learning can often be solved; in practice, the results suggest inherently new approaches for machine learning.

2014-04-23 - nltk - hierarchical classification + topic model training data for internet articles and social media - Stack Overflow
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2014-04-23 - topic-modeling-tool - A graphical user interface tool for topic modeling - Google Project Hosting
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2014-04-23 - Topic modeling with network regularization
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In this paper, we formally define the problem of topic modeling with network structure (TMN). We propose a novel solution to this problem, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data. The proposed method combines topic modeling and social network analysis, and leverages the power of both statistical topic models and discrete regularization. The output of this model can summarize well topics in text, map a topic onto the network, and discover topical communities.

2014-04-23 - Real-Time Topic Modeling of Microblogs
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2014-04-22 - topic-modeling-tool - A graphical user interface tool for topic modeling - Google Project Hosting
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2013-09-03 - Modeling the Evolution of Science
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2013-08-29 - Probabilistic Topic Models - blei-mlss-2012.pdf (slides)
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2013-08-21 - Probabilistic Topic Models
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The LSA approach makes three claims: that semantic information can be derived from a word-document co-occurrence matrix; that dimensionality reduction is an essential part of this derivation; and that words and documents can be represented as points in Euclidean space. Topic models' approach is consistent with the first two of these claims, but differs in the third, describing a class of statistical models in which the semantic properties of words and documents are expressed in terms of probabilistic topics.

2013-08-20 - The Remaking of Reading: Data Mining and the Digital Humanities
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2013-08-20 - Probabilistic Topic Models
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2013-08-20 - David M. Blei: Topic modeling
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links to introductory materials, corpus browsers based on topic models, and open source software (from my research group) for topic modeling.

2013-08-19 - Topic Modeling for Humanists: A Guided Tour » the scottbot irregular
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2012-09-20 - Getting Started with Topic Modeling and MALLET
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- what topic modeling is and why you might want to employ it

- how to install and work with the MALLET natural language processing toolkit to do so

2012-09-20

Properties

- sl:creationDate : 2012-09-20
- sl:creationTime : 2012-09-20T10:47:34Z
- sl:describedBy : https://en.wikipedia.org/wiki/Topic_model
- rdf:type : sl:Tag
- skos:altLabel : Topic model@fr
- skos:prefLabel : Topic Modeling