Neural Comput - Feature selection for ordinal text classification.

Tópicos

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Resumo

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.

Resumo Limpo

ordin classif also known ordin regress supervis learn task consist estim rate data item fix discret rate scale problem receiv increas attent sentiment analysi opinion mine communiti due import automat rate larg amount product review data digit form supervis learn task binari multiclass classif featur select often need order improv effici avoid overfit howev although featur select extens studi classif task ordin classif letter present six novel featur select method specif devis ordin classif test two data set product review data three method previous known literatur use two learn algorithm support vector regress tradit experiment result show six propos metric larg outperform three baselin techniqu stabl other order magnitud data set learn algorithm

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