Text Classification using Different Feature Extraction Approaches
Robert Dzisevic, Dmitrij Šešok · 2019
In this paper, we examine the results of applying three different text feature extraction approaches while classifying short sentences and phrases into categories with a neural network in order to find out which method is best at capturing text features and allows the classifier to achieve highest accuracy. The examined feature extraction methods include a plain Term Frequency Inverse Document Frequency (TF-IDF) approach and its two modifications by applying different dimensionality reduction techniques: Latent Semantic Analysis (LSA) and Linear Discriminant Analysis (LDA). The results show that the TF-IDF feature extraction approach outperforms other methods allowing the classifier to achieve highest accuracy when working with larger datasets. Furthermore, the results show that the TF-IDF in combination with LSA approach allows the classifier to achieve similar accuracy while working with smaller datasets.