Latent dirichlet allocation
Reads0
Chats0
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
More filters
Journal ArticleDOI
Event detection over twitter social media streams
Xiangmin Zhou,Lei Chen +1 more
TL;DR: A novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions is proposed, and a variable dimensional extendible hash over social streams is proposed.
Proceedings Article
Classifying Political Orientation on Twitter: It’s Not Easy!
Raviv Cohen,Derek Ruths +1 more
TL;DR: Standard techniques for inferring political orientation show that methods which previously reported greater than 90% inference accuracy, actually achieve barely 65% accuracy on normal users, and show that classifiers cannot be used to classify users outside the narrow range of political orientation on which they were trained.
Posted Content
Improving Topic Models with Latent Feature Word Representations
TL;DR: Two different Dirichlet multinomial topic models are extended by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus.
Proceedings ArticleDOI
Beyond Binary Labels: Political Ideology Prediction of Twitter Users
TL;DR: This study examines users’ political ideology using a seven-point scale which enables it to identify politically moderate and neutral users – groups which are of particular interest to political scientists and pollsters.
Proceedings ArticleDOI
Clustering the tagged web
TL;DR: It is demonstrated how user-generated tags from large-scale social bookmarking websites such as del.icio.us can be used as a complementary data source to page text and anchor text for improving automatic clustering of web pages.
References
More filters
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Book
Theory of probability
Harold Jeffreys,R. Bruce Lindsay +1 more
TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.