Hasil untuk "Analysis"

Alejandro Quiroz Flores

Quantitative social scientists use survival analysis to understand the forces that determine the duration of events. This Element provides a guideline to new techniques and models in survival analysis, particularly in three areas: non-proportional covariate effects, competing risks, and multi-state models. It also revisits models for repeated events. The Element promotes multi-state models as a unified framework for survival analysis and highlights the role of general transition probabilities as key quantities of interest that complement traditional hazard analysis. These quantities focus on the long term probabilities that units will occupy particular states conditional on their current state, and they are central in the design and implementation of policy interventions.

Maite Taboada, Julian Brooke, Milan Tofiloski et al.

We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and in completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.

U. Lee

Éric Brier, Christophe Clavier, Francis Olivier

G. Mills, Peter W. Airasian

H. Kohut

L. Anselin, I. Syabri, Youngihn Kho

C. Krebs

D. Bates, D. Watts

C. Cornell

S. Greenhouse, S. Geisser

岡藤 博国, 阿部 長門, 伊藤 達也

J. Hiriart-Urruty, C. Lemaréchal

Thomas Hofmann

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of experiments.

W. Horwitz

A. Stuart, D. R. Cox

G. Ryan, H. Bernard

J. Rice

D. Stroup, J. Berlin, S. Morton et al.

E. Ziegel, Lyman Ott