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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Journal ArticleDOI
Open challenges for data stream mining research
Georg Krempl,Indre Žliobaite,Dariusz Brzezinski,Eyke Hüllermeier,Vincent Lemaire,Tino Noack,Ammar Shaker,Sonja Sievi,Myra Spiliopoulou,Jerzy Stefanowski +9 more
TL;DR: This article presents a discussion on eight open challenges for data stream mining, which cover the full cycle of knowledge discovery and involve such problems as protecting data privacy, dealing with legacy systems, handling incomplete and delayed information, analysis of complex data, and evaluation of stream mining algorithms.
Journal ArticleDOI
Interactive Topic Modeling
TL;DR: This paper presents a mechanism for giving users a voice by encoding users’ feedback to topic models as correlations between words into a topic model, and develops more efficient inference algorithms for tree-based topic models.
Journal ArticleDOI
Statistical topic models for multi-label document classification
TL;DR: The experimental results indicate that probabilistic generative models can achieve competitive multi-label classification performance compared to discriminative methods, and have advantages for datasets with many labels and skewed label frequencies.
Proceedings ArticleDOI
Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
TL;DR: This paper applies a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimates the aspect importance of a user towards an item, and introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM.
Proceedings ArticleDOI
Recognizing Depression from Twitter Activity
TL;DR: This paper extensively evaluates the effectiveness of using a user's social media activities for estimating degree of depression, and extracts several features from the activity histories of Twitter users to construct models for estimating the presence of active depression.
References
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