References

Alkomah, Fatimah, and Xiaogang Ma. 2022. “A Literature Review of Textual Hate Speech Detection Methods and Datasets.” Information 13 (6, 6): 273. https://doi.org/10.3390/info13060273.

Almeida, Felipe, and Geraldo Xexéo. 2019. “Word Embeddings: A Survey.” ArXiv, January. https://www.semanticscholar.org/paper/Word-Embeddings%3A-A-Survey-Almeida-Xex%C3%A9o/e28e81a8cb6655aebb72357538f7b7a360366a29.

Barry, Paul. 2017. Python von Kopf bis Fuß. Translated by Jørgen W. Lang. Zweite Auflage. Von Kopf bis Fuß. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.

Camacho-Collados, Jose, and Mohammad Taher Pilehvar. 2020. “Embeddings in Natural Language Processing.” In Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts, 10–15. Barcelona, Spain (Online): International Committee for Computational Linguistics. https://doi.org/10.18653/v1/2020.coling-tutorials.2.

Castaño-Pulgarín, Sergio Andrés, Natalia Suárez-Betancur, Luz Magnolia Tilano Vega, and Harvey Mauricio Herrera López. 2021. “Internet, Social Media and Online Hate Speech. Systematic Review.” Aggression and Violent Behavior 58 (May): 101608. https://doi.org/10.1016/j.avb.2021.101608.

Chollet, François. 2021. Deep Learning with Python. Second edition. Shelter Island, NY: Manning.

Chollet, François, Tomasz Kalinowski, and J. J. Allaire. 2022a. Deep Learning with R. Second edition. Shelter Island, NY: Manning.

———. 2022b. Deep Learning with R. Second edition. Shelter Island, NY: Manning Publications Co.

Downey, Allen B. 2021. Think Python: systematisch programmieren lernen mit Python. Translated by Peter Klicman. 1. Auflage. Heidelberg: O’Reilly.

Gallatin, Kyle, and Chris Albon. 2023. Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly Media.

George, Alexandra. 2022. Python Text Mining: Perform Text Processing, Word Embedding, Text Classification and Machine Translation. Delhi: BPB Publications.

Géron, Aurélien. 2023a. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Third edition. Beijing Boston Farnham Sebastopol Tokyo: O’Reilly.

———. 2023b. Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. Translated by Kristian Rother and Thomas Demmig. 3., aktualisierte und erweiterte Auflage. Heidelberg: O’Reilly.

———. 2023c. Praxiseinstieg Machine Learning mit Scikit-Learn, Keras und TensorFlow: Konzepte, Tools und Techniken für intelligente Systeme. Translated by Kristian Rother and Thomas Demmig. 3., aktualisierte und erweiterte Auflage. Heidelberg: O’Reilly.

Hunt, Andrew, and David Thomas. 2000. The Pragmatic Programmer from Journeyman to Master. Reading, Mass.: Addison-Wesley.

Hvitfeldt, Emil, and Julia Silge. 2021. Supervised Machine Learning for Text Analysis in R. 1st ed. Boca Raton: Chapman and Hall/CRC. https://doi.org/10.1201/9781003093459.

Inden, Michael. 2023. Python lernen: kurz & gut. 1. Auflage. O’Reillys Taschenbibliothek. Heidelberg: O’Reilly.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in R. Second edition. Springer Texts in Statistics. New York: Springer. https://link.springer.com/book/10.1007/978-1-0716-1418-1.

König, Tim, Wolf J. Schünemann, Alexander Brand, Julian Freyberg, and Michael Gertz. 2022. “The EPINetz Twitter Politicians Dataset 2021. A New Resource for the Study of the German Twittersphere and Its Application for the 2021 Federal Elections.” Politische Vierteljahresschrift 63 (3): 529–47. https://doi.org/10.1007/s11615-022-00405-7.

Kulkarni, Akshay, and Adarsha Shivananda. 2021. Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning Using Python. Second edition. New York: Apress.

Kurz, A. Solomon. 2021. Statistical Rethinking with Brms, Ggplot2, and the Tidyverse: Second Edition. https://bookdown.org/content/4857/.

Lex, Alexander, Nils Gehlenborg, Hendrik Strobelt, Romain Vuillemot, and Hanspeter Pfister. 2014. “UpSet: Visualization of Intersecting Sets.” IEEE Transactions on Visualization and Computer Graphics 20 (12): 1983–92. https://doi.org/10.1109/TVCG.2014.2346248.

Liu, Zhiyuan, Yankai Lin, and Maosong Sun, eds. 2023. Representation Learning for Natural Language Processing. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1600-9.

McElreath, Richard. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. 2nd ed. CRC Texts in Statistical Science. Boca Raton: Taylor and Francis, CRC Press.

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. “Efficient Estimation of Word Representations in Vector Space.” September 6, 2013. https://doi.org/10.48550/arXiv.1301.3781.

Pennington, Jeffrey, Richard Socher, and Christopher Manning. 2014. “GloVe: Global Vectors for Word Representation.” In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–43. Doha, Qatar: Association for Computational Linguistics. https://doi.org/10.3115/v1/D14-1162.

Pilehvar, Mohammad Taher, and Jose Camacho-Collados. 2021. Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning. Synthesis Lectures on Human Language Technologies. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-02177-0.

Remus, Robert, Uwe Quasthoff, and Gerhard Heyer. 2010. “SentiWS - a Publicly Available German-Language Resource for Sentiment Analysis.” Proceedings of the 7th International Language Ressources and Evaluation (LREC’10), 1168–71.

Rhys, Hefin. 2020. Machine Learning with R, the Tidyverse, and Mlr. Shelter Island, NY: Manning publications.

Risch, Julian, Anke Stoll, Lena Wilms, and Michael Wiegand. 2021. “Overview of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments.” In Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments, 1–12. Duesseldorf, Germany: Association for Computational Linguistics. https://aclanthology.org/2021.germeval-1.1.

Rothman, Denis. 2022. Transformers for Natural Language Processing: Build, Train, and Fine-Tune Deep Neural Network Architectures for NLP with Python, Hugging Face, and OpenAI´s GPT3, ChatGPT, and GPT-4. Second edition. Expert Insight. Birmingham Mumbai: Packt.

Shannon, C. E. 1948. “A Mathematical Theory of Communication.” Bell System Technical Journal 27 (3): 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.

Siegel, Melanie, and Melpomeni Alexa. 2020. Sentiment-Analyse deutschsprachiger Meinungsäußerungen: Grundlagen, Methoden und praktische Umsetzung. Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-29699-5.

Silge, Julia, and David Robinson. 2017. Text Mining with R: A Tidy Approach. First edition. Beijing ; Boston: O’Reilly. https://www.tidytextmining.com/.

Stone, James V. 2019. “Information Theory: A Tutorial Introduction.” June 13, 2019. http://arxiv.org/abs/1802.05968.

Tunstall, Lewis, Leandro von Werra, Thomas Wolf, and Aurélien Géron. 2022. Natural Language Processing with Transformers: Building Language Applications with Hugging Face. Revised edition. Sebastopol: O’Reilly.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2023. “Attention Is All You Need.” August 1, 2023. https://doi.org/10.48550/arXiv.1706.03762.

Wickham, Hadley, and Garrett Grolemund. 2016. R for Data Science: Visualize, Model, Transform, Tidy, and Import Data. O’Reilly Media. https://r4ds.had.co.nz/index.html.

Wiegand, Michael. 2019a. “GermEval-2018 Corpus (DE).” heiDATA. https://doi.org/10.11588/data/0B5VML.

———. 2019b. “GermEval-2018 Corpus (DE).” heiDATA. https://doi.org/10.11588/data/0B5VML.

———. 2019c. “GermEval-2018-Data-master.” In GermEval-2018 Corpus (DE). heiDATA. https://doi.org/10.11588/data/0B5VML/XIUWJ7.

“Word Embeddings in NLP: A Complete Guide.” 2023. Turing. 2023. https://www.turing.com/kb/guide-on-word-embeddings-in-nlp.

Yamada, Ikuya, and Hiroyuki Shindo. 2019. “Neural Attentive Bag-of-Entities Model for Text Classification.” In Proceedings of the 23th SIGNLL Conference on Computational Natural Language Learning, 563–73. Association for Computational Linguistics.