nach oben

Erschienen in:

2021 | OriginalPaper | Buchkapitel

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

verfasst von : André Wendland, Marco Zenere, Jörg Niemann

Erschienen in: Systems, Software and Services Process Improvement

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Gamification Framework in Automotive SW Development Environment to Increase Teams Engagement

Nächstes Kapitel Digital Transformation and the Role of Dynamic Tooling in Extracting Microservices from Existing Software Systems

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

SPI manifesto. https://2020.eurospi.net/index.php/manifesto. Accessed 01 Feb 2021

Bovet, A., Makse, H.A.: Influence of fake news in Twitter during the 2016 US presidential election. Nat Commun 10(1), 7 (2019). https://doi.org/10.1038/s41467-018-07761-2CrossRef

Boldyreva, E.L.: Cambridge analytica: ethics and online manipulation with decision-making process, pp. 91–102, December 2018. https://doi.org/10.15405/epsbs.2018.12.02.10

Goodman, S.K.: Information needs for management decision-making. ARMA Rec. Manage. Q. 27(4), 12 (1993)

Fake and real news dataset. https://kaggle.com/clmentbisaillon/fake-and-real-news-dataset. Accessed 05 May 2020

Biba, M., Gjati, E.: Boosting Text Classification through Stemming of Composite Words. In: Thampi, S.M., Abraham, A., Pal, S.K., Rodriguez, J.M.C. (eds.) Recent Advances in Intelligent Informatics, vol. 235, pp. 185–194. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-01778-5_19CrossRef

Hakim, A.A., Erwin, A., Eng, K.I., Galinium, M., Muliady, W.: Automated document classification for news article in Bahasa Indonesia based on term frequency inverse document frequency (TF-IDF) approach. In; 2014 6th International Conference on Information Technology and Electrical Engineering (ICITEE), Yogyakarta, Indonesia, pp. 1–4, October 2014. https://doi.org/10.1109/ICITEED.2014.7007894

Dasgupta, S., Goldberg, Y., Kosorok, M.: Feature elimination in kernel machines in moderately high dimensions. arXiv:1304.5245 [stat], December 2015. http://arxiv.org/abs/1304.5245. Accessed 07 May 2020

Scikit-learn: machine learning in Python—scikit-learn 0.22.2 documentation. https://scikit-learn.org/stable/. Accessed 12 May 2020

10.

Keras: the Python deep learning API. https://keras.io/. Accessed 12 May 2020

11.

Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., Brown, D.: Text classification algorithms: a survey. Information 10(4), 150 (2019). https://doi.org/10.3390/info10040150CrossRef

12.

Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683CrossRef

13.

Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.: Deep learning based text classification: a comprehensive review. arXiv:2004.03705 [cs, stat], April 2020. http://arxiv.org/abs/2004.03705. Accessed 12 May 2020

14.

A Beginner’s Guide to Bag of Words & TF-IDF. Pathmind. http://pathmind.com/wiki/bagofwords-tf-idf. Accessed 12 May 2020

15.

Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Deep learning. In: Data Mining, pp. 417–466. Elsevier (2017). https://doi.org/10.1016/B978-0-12-804291-5.00010-6

16.

Sharma, H., Kumar, S.: A survey on decision tree algorithms of classification in data mining, April 2016. https://www.researchgate.net/publication/324941161_A_Survey_on_Decision_Tree_Algorithms_of_Classification_in_Data_Mining

17.

Poddar, K., Amali D, G.B., Umadevi, K.S.: Comparison of various machine learning models for accurate detection of fake news. In: 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, pp. 1–5, March 2019. https://doi.org/10.1109/i-PACT44901.2019.8960044.

18.

Aphiwongsophon, S., Chongstitvatana, P.: Detecting fake news with machine learning method. In: 2018 15th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Rai, Thailand, pp. 528–531, July 2018. https://doi.org/10.1109/ECTICon.2018.8620051

19.

Ahmed, H., Traore, I., Saad, S.: Detection of online fake news using n-gram analysis and machine learning techniques. In: Traore, I., Woungang, I., Awad, A. (eds.) ISDDC 2017. LNCS, vol. 10618, pp. 127–138. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69155-8_9CrossRef

20.

Regularization for Simplicity: Lambda | Machine Learning Crash Course. https://developers.google.com/machine-learning/crash-course/regularization-for-simplicity/lambda. Accessed 12 May 2020

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

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

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

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner