# Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis

@inproceedings{Tang2014UnderstandingTL, title={Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis}, author={Jian Tang and Zhaoshi Meng and XuanLong Nguyen and Qiaozhu Mei and Ming Zhang}, booktitle={ICML}, year={2014} }

Topic models such as the latent Dirichlet allocation (LDA) have become a standard staple in the modeling toolbox of machine learning. They have been applied to a vast variety of data sets, contexts, and tasks to varying degrees of success. However, to date there is almost no formal theory explicating the LDA's behavior, and despite its familiarity there is very little systematic analysis of and guidance on the properties of the data that affect the inferential performance of the model. This…

## 204 Citations

Model Selection for Topic Models via Spectral Decomposition

- Computer ScienceAISTATS
- 2015

This work derives the upper bound and lower bound on the number of topics given a text collection of finite size under mild conditions and shows that its methodology can be easily generalized to model selection analysis for other latent models.

Guaranteed inference in topic models

- Computer Science
- 2015

This paper introduces a provably fast algorithm, namely Online Maximum a Posteriori Estimation (OPE), for posterior inference in topic models, and employs OPE to design three methods for learning Latent Dirichlet Allocation from text streams or large corpora.

Examining the Coherence of the Top Ranked Tweet Topics

- Computer ScienceSIGIR
- 2016

Evidence is found that Twitter LDA outperforms both LDA and the tweet pooling method because the top ranked topics it generates have more coherence; it is demonstrated that a larger number of topics helps to generate topics with more coherent; and coherence at n is shown to be more effective when evaluating the coherence of a topic model than the average coherence score.

Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling

- Computer ScienceJournal of Computational and Graphical Statistics
- 2019

A variant of the Metropolis–Hastings algorithm is presented that can be used to estimate the posterior distribution of the number of topics and it is evaluated on synthetic data and with procedures that are currently used in the machine learning literature.

Most large topic models are approximately separable

- Computer Science, Mathematics2015 Information Theory and Applications Workshop (ITA)
- 2015

It is proved that when the columns of the topic matrix are independently sampled from a Dirichlet distribution, the resulting topic matrix will be approximately separable with probability tending to one as the number of rows (vocabulary size) scales to infinity sufficiently faster than thenumber of columns (topics).

Dual online inference for latent Dirichlet allocation

- Computer ScienceACML
- 2014

It is shown that OFW converges to some local optima, but under certain conditions it can converge to global optima and can be readily employed to accelerate the MAP estimation in a wide class of probabilistic models.

Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge

- Computer ScienceTACL
- 2017

Correlation Explanation is introduced, an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework that generalizes to hierarchical and semi-supervised extensions with no additional modeling assumptions.

Topic modeling in marketing: recent advances and research opportunities

- Business, Computer ScienceJournal of Business Economics
- 2018

This work characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation, and confirms that there is considerable progress done in various marketing sub-areas.

Optimisation towards Latent Dirichlet Allocation: Its Topic Number and Collapsed Gibbs Sampling Inference Process

- Computer ScienceInternational Journal of Electrical and Computer Engineering (IJECE)
- 2018

The results show that the maximum likelihood and MDL approach result in the same number of optimal topics, and the highest average accuracy is 61% with alpha 0.1 and beta 0.001.

A Model of Text for Experimentation in the Social Sciences

- Computer Science
- 2016

A hierarchical mixed membership model for analyzing topical content of documents, in which mixing weights are parameterized by observed covariates is posit, enabling researchers to introduce elements of the experimental design that informed document collection into the model, within a generally applicable framework.

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