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2021 | OriginalPaper | Chapter

Introduction to Text Classification: Impact of Stemming and Comparing TF-IDF and Count Vectorization as Feature Extraction Technique

Authors : André Wendland, Marco Zenere, Jörg Niemann

Published in: Systems, Software and Services Process Improvement

Publisher: Springer International Publishing

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Abstract

Natural language processing is a widely used application in research and industry. Amongst other, use cases are sentiment analysis, speech recognition, classification, query answering and machine translation. In this research we investigate widely applied preprocessing methods, to improve the results of different Algorithms trained on a Fake News data set. As feature extraction methods we compared TF-IDF and Count-Vectorization. TF-IDF yielded slightly better results in terms of accuracy. We found that, as opposed to current research, stemming leads to a minor increase of false positive and false negative classifications, hence to a decrease in accuracy. Among the compared models, logistic regression and support vector machine yielded the best results.

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The dataset is not completely balanced and contains more true news than fake news.

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Title: Introduction to Text Classification: Impact of Stemming and Comparing TF-IDF and Count Vectorization as Feature Extraction Technique
Authors: André Wendland
Marco Zenere
Jörg Niemann
Publisher: Springer International Publishing
Book: Systems, Software and Services Process Improvement
Print ISBN: 978-3-030-85520-8

Electronic ISBN: 978-3-030-85521-5

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-030-85521-5_19

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