PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Baccianella S., Esuli A., Sebastiani F. Feature selection for ordinal text classification. In: Neural Computation, vol. 26 (3) pp. 557 - 591. The MIT Press, 2014.
 
 
Abstract
(English)
Ordinal classification (also known as ordinal regression) is a supervised learning task that consists of estimating the rating of a data item on a fixed, discrete rating scale. This problem is receiving increased attention from the sentiment analysis and opinion mining community due to the importance of automatically rating large amounts of product review data in digital form. As in other supervised learning tasks such as binary or multiclass classification, feature selection is often needed in order to improve efficiency and avoid overfitting. However, although feature selection has been extensively studied for other classification tasks, it has not for ordinal classification. In this letter, we present six novel feature selection methods that we have specifically devised for ordinal classification and test them on two data sets of product review data against three methods previously known from the literature, using two learning algorithms from the support vector regression tradition. The experimental results show that all six proposed metrics largely outperform all three baseline techniques (and are more stable than these others by an order of magnitude), on both data sets and for both learning algorithms.
URL: http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00558
DOI: 10.1162/NECO_a_00558
Subject Ordinal regression
Feature selection
Text classification
I.2.6 Learning


Icona documento 1) Download Document PDF


Icona documento Open access Icona documento Restricted Icona documento Private

 


Per ulteriori informazioni, contattare: Librarian http://puma.isti.cnr.it

Valid HTML 4.0 Transitional