… practice three different text feature extraction approaches by … The TF-IDF feature extraction
approach is found to … to classify text just as good as using plain TF-IDF feature extraction. …

… We introduce novel feature extraction methods to harness the emotion rich knowledge being
… , here we extract features to classify text into emotion classes using machine learning; and …

… a comprehensive review on feature selection techniques for text classification. We begin
by … measures used in text classification. Then, we review the most popular text classifiers, …

… In reference [87], this paper proposes a novel hybrid text classification model based on the
deep belief network and softmax regression. The experimental results on Reuters-21578 and …

… the performance of classification algorithms using Scopus dataset. In text classification,
classification and feature extraction from the document using extracted features are the major …

… This article reviews and discusses feature extraction techniques used for text classification …
optimize feature extraction. In feature extraction, a specific of words is taken out from text data. …

… features. To the best of our knowledge, we are the first to incorporate global representation
into the extraction procedure of local features for text classification… regions feature extraction …

… of text classification. First, we cover traditional methods of text classification, such as Rocchio
classification. Next… Furthermore, we discuss two common methods of text feature extraction: …

… before the feature extraction. Since feature extraction in text classification generally results in
… dimension reduction to use classification algorithms efficiently. After dimension reduction, a …

… for the temporal event extraction, in this work they prove that the influence of undocumented
clinical texts helps to significantly increase the accuracy of temporal event extraction. But it …