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324
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
 In Proc. 43st ACL
, 2005
"... We address the ratinginference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multipoint scale (e.g., one to five “stars”). This task represents an int ..."
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Cited by 283 (2 self)
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We address the ratinginference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multipoint scale (e.g., one to five “stars”). This task represents an interesting twist on standard multiclass text categorization because there are several different degrees of similarity between class labels; for example, “three stars ” is intuitively closer to “four stars ” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm, based on a metric labeling formulation of the problem, that alters a givenary classifier’s output in an explicit attempt to ensure that similar items receive similar labels. We show that the metaalgorithm can provide significant improvements over both multiclass and regression versions of SVMs when we employ a novel similarity measure appropriate to the problem. 1
Gaussian processes for ordinal regression
 Journal of Machine Learning Research
, 2004
"... We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation ..."
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Cited by 117 (4 self)
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We present a probabilistic kernel approach to ordinal regression based on Gaussian processes. A threshold model that generalizes the probit function is used as the likelihood function for ordinal variables. Two inference techniques, based on the Laplace approximation and the expectation propagation algorithm respectively, are derived for hyperparameter learning and model selection. We compare these two Gaussian process approaches with a previous ordinal regression method based on support vector machines on some benchmark and realworld data sets, including applications of ordinal regression to collaborative filtering and gene expression analysis. Experimental results on these data sets verify the usefulness of our approach.
Learning with Matrix Factorization
, 2004
"... Matrices that can be factored into a product of two simpler matrices can serve as a useful and often natural model in the analysis of tabulated or highdimensional data. Models based on matrix factorization (Factor Analysis, PCA) have been extensively used in statistical analysis and machine learning ..."
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Cited by 76 (6 self)
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Matrices that can be factored into a product of two simpler matrices can serve as a useful and often natural model in the analysis of tabulated or highdimensional data. Models based on matrix factorization (Factor Analysis, PCA) have been extensively used in statistical analysis and machine learning for over a century, with many new formulations and models suggested in recent
Prediction of Ordinal Classes Using Regression Trees
, 2001
"... This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with SCART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm ..."
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Cited by 41 (0 self)
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This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes using classification and regression trees. We start with SCART, a tree induction algorithm, and study various ways of transforming it into a learner for ordinal classification tasks. These algorithm variants are compared on a number of benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the tradeoff between optimal categorical classification accuracy (hit rate) and minimum distancebased error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression.
Sequential Ordinal Modeling with Applications to Survival Data
 Biometrics
, 2001
"... This paper considers the class of sequential probit models in relation to other models for ordinal data. Hierarchical and other extensions of the model are proposed for applications involving discrete time (grouped) survival data. Computationally practical Markov chain Monte Carlo algorithms are dev ..."
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Cited by 33 (2 self)
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This paper considers the class of sequential probit models in relation to other models for ordinal data. Hierarchical and other extensions of the model are proposed for applications involving discrete time (grouped) survival data. Computationally practical Markov chain Monte Carlo algorithms are developed for the fitting of these models. The ideas and methods are illustrated in detail with a real data example on the length of hospital stay for patients undergoing heart surgery. A notable aspect of this analysis is the comparison, based on marginal likelihoods and training sample priors, of several nonnested models, such as the sequential model, the cumulative ordinal model and Weibull and loglogistic models. Keywords: Bayes factor; Discrete hazard function; Gibbs sampling; Marginal likelihood; MetropolisHastings algorithm; Nonnested models; Sequential probit; Training sample prior; Model comparison. 1 Introduction Ordinal response data is generally analyzed using the cumulative o...
Magnitudepreserving ranking algorithms
, 2007
"... This paper studies the learning problem of ranking when one wishes not just to accurately predict pairwise ordering but also preserve the magnitude of the preferences or the difference between ratings, a problem motivated by its key importance in the design of search engines, movie recommendation, a ..."
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Cited by 33 (3 self)
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This paper studies the learning problem of ranking when one wishes not just to accurately predict pairwise ordering but also preserve the magnitude of the preferences or the difference between ratings, a problem motivated by its key importance in the design of search engines, movie recommendation, and other similar ranking systems. We describe and analyze several algorithms for this problem and give stability bounds for their generalization error, extending previously known stability results to nonbipartite ranking and magnitude of preferencepreserving algorithms. We also report the results of experiments comparing these algorithms on several datasets and compare these results with those obtained using an algorithm minimizing the pairwise misranking error and standard regression. 1.
An Analysis of Statistical Models and Features for Reading Difficulty Prediction
"... A reading difficulty measure can be described as a function or model that maps a text to a numerical value corresponding to a difficulty or grade level. We describe a measure of readability that uses a combination of lexical features and grammatical features that are derived from subtrees of syntact ..."
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Cited by 31 (1 self)
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A reading difficulty measure can be described as a function or model that maps a text to a numerical value corresponding to a difficulty or grade level. We describe a measure of readability that uses a combination of lexical features and grammatical features that are derived from subtrees of syntactic parses. We also tested statistical models for nominal, ordinal, and interval scales of measurement. The results indicate that a model for ordinal regression, such as the proportional odds model, using a combination of grammatical and lexical features is most effective at predicting reading difficulty. 1
0407 Income and Happiness: New Results from Generalized Threshold and Sequential Models Stefan Boes and
, 2004
"... Empirical studies on the relationship between income and happiness commonly use standard ordered response models, the most wellknown representatives being the ordered logit and the ordered probit. However, these models restrict the marginal probability effects by design, and therefore limit the ana ..."
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Cited by 28 (2 self)
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Empirical studies on the relationship between income and happiness commonly use standard ordered response models, the most wellknown representatives being the ordered logit and the ordered probit. However, these models restrict the marginal probability effects by design, and therefore limit the analysis of distributional aspects of a change in income, that is, the study of whether the income effect depends on a person’s happiness. In this paper we pinpoint the shortcomings of standard models and propose two alternatives, namely generalized threshold and sequential models. With data of two waves of the German SocioEconomic Panel, 1984 and 1997, we show that the more general models yield different marginal probability effects than standard models. JEL Classification: C25, I31
Seminonparametric Estimation of Extended Ordered Probit Models
, 2003
"... This paper presents a seminonparametric estimator for a series of generalized models that nest the Ordered Probit model and thereby relax the distributional assumptions in that model. It describes a new Stata command for the estimation of such models and presents an illustration of the approach. ..."
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Cited by 28 (0 self)
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This paper presents a seminonparametric estimator for a series of generalized models that nest the Ordered Probit model and thereby relax the distributional assumptions in that model. It describes a new Stata command for the estimation of such models and presents an illustration of the approach.
A Hierarchical Latent Variable Model for Ordinal Data with "No Answer" Responses
 Journal of the American Statistical Association
, 1997
"... An item response theory model for ordinal responses proposes that the probability of a particular response from a person on an specific item is a function of latent person and question parameters and of cutoffs for the ordinal response categories. This structure was incorporated into a Bayesian hier ..."
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Cited by 25 (0 self)
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An item response theory model for ordinal responses proposes that the probability of a particular response from a person on an specific item is a function of latent person and question parameters and of cutoffs for the ordinal response categories. This structure was incorporated into a Bayesian hierarchical model by Albert and Chib (1993). We extend their formulation by modeling "No Answer" responses as due to either lack of a strong opinion or indifference to the entire question. In our hierarchical Bayesian framework, prior means for the person and item effects are related to observed covariates. An application of the model to the DuPont Corporation 1992 Engineering Polymers Division Customer Satisfaction Survey is described in detail. The nonconjugate likelihood and prior prevent closed form posterior inference. Three different iterative solutions, using the Griddy Gibbs Sampler (Ritter and Tanner 1992), MetropolisHastings Algorithm (Roberts and Smith 1993), and Data Augmen...