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 …

Advancement in information technology produced massive textual material that is available
online. Text classification algorithms are at the core of many natural language processing …

Text document classification is an important task for diverse natural language processing
based applications. Traditional machine learning approaches mainly focused on reducing …

Text classification is one of the research hotspots in the field of Natural Language
Processing (NLP). In this paper, TextCNN model based on Convolutional Neural Network …

The TF-IDF (term frequency-inverse document frequency) algorithm is based on word
statistics for text feature extraction. Which considers only the expressions of words that are …

Any Text Classification (TC) problem need pre-processing steps which may affect the
classification accuracy. Especially pre-processing steps need substantial effort particularly in …

Text classification is an important task which may help human reducing time and effort. This
work is aimed to propose an approach for text classification, especially for articles. The …

In the recent years, the categorization of text documents into predefined classifications has
perceived a growing interest due to the growing of documents in digital form and needs to …

Feature extraction in the field of Text Processing or Natural Language Processing (NLP) has
its own challenges due to the characteristics of unstructured text. Thus, the selection of the …

Text Classification is the process of categorizing text into the relevant categories and its
algorithms are at the core of many Natural Language Processing (NLP). Term Frequency …