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Business and Social Statistics
Department of Statistics
Studenten im Sommer auf dem Campus Nord, rote Zahnräder im Hintergrund © Roland Baege​/​TU Dortmund
Doctoral Student

M.Sc. Kai-Robin Lange

Contact

TU Dortmund University
Department of Statistics
Chair of Business- and Social Statistics
CDI Building, Room 4
44221 Dortmund
Germany

E-Mail: kalangestatistik.tu-dortmundde
Phone: +49 231 755 5477

Portrait photo of Kai-Robin Lange © Kai-Robin Lange​/​private
  • since November 2021: Doctoral Student at the Chair of Buisness and Social Statistics
  • 2020-2021: Research Assistant at the Chair of Buisness and Social Statistics
  • 2019-2021: M.Sc. Statistics with minor in Computer Science, TU Dortmund University
    Master Thesis: Resampling strategies for unsupervised sentiment analysis using lexicon-based text embedding methods
  • 2016-2019: B.Sc. Statistics with minor in Computer Science, TU Dortmund University
  • Research in Text Mining
    • Content analysis of political debates, speeches and documents
    • Evaluation of embedding-based methods
    • Extraction of events and narratives in text corpora
  • Additional methodical interests
    • Resampling procedures
    • Boosting
    • Causal inference

GitHub profile

ORCiD, Google Scholar

  • Lange, K.-R., Rieger, J. und Jentsch, C. (2024). Lex2Sent: A bagging approach to unsupervised sentiment analysis. Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024), 281-291. Link.
  • Lange, K.-R., Jentsch, C. (2023). SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments. Proceedings of the 3rd Workshop on Computational Linguistics for Political Text Analysis. Poster. pdf. download.
  • Lange, K.-R., Rieger, J., Benner, N. and Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis, 47-53. pdf. GitHub.
  • Rieger, J., Lange, K.-R., Flossdorf, J. and Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story'22 Workshop. CEUR-WS 3117, 5-13. pdf. GitHub.

Pre-prints and Working paper

  • Lange, K.-R., Reccius, M., Schmidt, T., Müller, H., Roos, M., and Jentsch, C. (2022).  Towards extracting collective economic narratives from texts. Ruhr Economic Papers #963. Link
  • Benner, N., Lange, K.-R., and Jentsch, C. (2022).  Named Entity Narratives. Ruhr Economic Papers #962. Link
  • "Lex2Sent: A bagging approach to unsupervised sentiment analysis." 20th Conference on Natural Language Processing (KONVENS 2024). Vienna, Austria (09/2024).
  • "Identifying economic narratives in large text corpora" Sixth International and Interdisciplinary Conference on the Quantitative and Computational Analysis of Textual Data (COMPTEXT) 2024. Amsterdam, Niederlande (05/2024).
  • "SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments". CPSS Workshop @KONVENS 2023. Potsdam, Germany (09/2023).
  • "Roll2Vec: updating diachronic embeddings with a rolling window". Statistische Woche 2023. Dortmund, Germany (09/2023).
  • "Zeitenwenden: Detecting changes in the German political discourse". CPSS Workshop @KONVENS 2022. Potsdam, Germany (09/2022).
  • Seminar: Advanced Text Mining Methods (SuSe 2024)
  • Natural Language Processing (WiSe 2023/24)
  • Seminar: Advanced Text Mining Methods (WiSe 2022/23)
  • Asymptotic Theory (WiSe 2022/23)
  • Text as Data (WiSe 2022/23)
  • Entscheidungstheorie (SuSe 2022)
  • Asymptotic Theory (WiSe 2021/22)
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