Latent dirichlet allocation
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TLDR
This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.Abstract:
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.read more
Citations
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Automated Text Analysis for Consumer Research
TL;DR: It is argued that although automated text analysis cannot be used to study all phenomena, it is a useful tool for examining patterns in text that neither researchers nor consumers can detect unaided.
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Aspect Extraction through Semi-Supervised Modeling
Arjun Mukherjee,Bing Liu +1 more
TL;DR: This paper proposes two statistical models to solve the seeded problem of aspect extraction in a different setting where the user provides some seed words for a few aspect categories and the model extracts and clusters aspect terms into categories simultaneously.
Proceedings ArticleDOI
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Proceedings Article
Automatic Keyphrase Extraction via Topic Decomposition
TL;DR: A Topical PageRank (TPR) is built on word graph to measure word importance with respect to different topics and shows that TPR outperforms state-of-the-art keyphrase extraction methods on two datasets under various evaluation metrics.
Proceedings Article
Tri-party deep network representation
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References
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