A new metric for feature selection on short text datasets

Abstract

In recent years, short texts are everywhere, especially in social media networks. Short text classification is an essential task for various applications related to the operations on short text documents. In many cases, using the entire feature set causes the high dimensionality problem in short text data. This problem reason of time-consuming and negatively impacts the performance of classifiers. This study presents an effective feature selection algorithm called XY method, which represents the features on XY line and calculates the distance of a feature to the XY line. Also, a value named λ is calculated. According to this value, the terms are divided into different regions such as negative, positive, and third to determine their discrimination capability. The novel XY method aims to select as few terms as possible in the negative region. The proposed method is evaluated using four different short text datasets with Macro-F1 success measure. In comparisons with other existing feature selection algorithms such as chi-square, information gain, deviation from Poisson distribution, recently proposed max-min ratio, and distinguishing feature selector demonstrate that the XY method achieves either better or competitive performance in significantly reduced various feature sizes.