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Documents authored by Neidhardt, Julia

Document
DOI: 10.4230/OASIcs.LDK.2021.37
A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis

Authors: Bettina M. J. Kern, Andreas Baumann, Thomas E. Kolb, Katharina Sekanina, Klaus Hofmann, Tanja Wissik, and Julia Neidhardt

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
The domain of German polarity dictionaries is heterogeneous with many small dictionaries created for different purposes and using different methods. This paper aims to map out the landscape of freely available German polarity dictionaries by clustering them to uncover similarities and shared features. We find that, although most dictionaries seem to agree in their assessment of a word’s sentiment, subsets of them form groups of interrelated dictionaries. These dependencies are in most cases an immediate reflex of how these dictionaries were designed and compiled. As a consequence, we argue that sentiment evaluation should be based on multiple and diverse sentiment resources in order to avoid error propagation and amplification of potential biases.
Cite as

Bettina M. J. Kern, Andreas Baumann, Thomas E. Kolb, Katharina Sekanina, Klaus Hofmann, Tanja Wissik, and Julia Neidhardt. A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 37:1-37:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{kern_et_al:OASIcs.LDK.2021.37,
  author =	{Kern, Bettina M. J. and Baumann, Andreas and Kolb, Thomas E. and Sekanina, Katharina and Hofmann, Klaus and Wissik, Tanja and Neidhardt, Julia},
  title =	{{A Review and Cluster Analysis of German Polarity Resources for Sentiment Analysis}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{37:1--37:17},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.37},
  URN =		{urn:nbn:de:0030-drops-145734},
  doi =		{10.4230/OASIcs.LDK.2021.37},
  annote =	{Keywords: cluster analysis, sentiment polarity, sentiment analysis, German, review}
}
Document
DOI: 10.4230/OASIcs.LDK.2021.38
Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data

Authors: Andreas Baumann, Klaus Hofmann, Bettina Kern, Anna Marakasova, Julia Neidhardt, and Tanja Wissik

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
We explore relationships between dynamics of emotion (arousal and valence) and topical stability in political discourse in two diachronic corpora of Austrian German. In doing so, we assess interactions among emotional and topical dynamics related to political parties as well as interactions between two different domains of discourse: debates in the parliament and journalistic media. Methodologically, we employ unsupervised techniques, time-series clustering and Granger-causal modeling to detect potential interactions. We find that emotional and topical dynamics in the media are only rarely a reflex of dynamics in parliamentary discourse.
Cite as

Andreas Baumann, Klaus Hofmann, Bettina Kern, Anna Marakasova, Julia Neidhardt, and Tanja Wissik. Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 38:1-38:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{baumann_et_al:OASIcs.LDK.2021.38,
  author =	{Baumann, Andreas and Hofmann, Klaus and Kern, Bettina and Marakasova, Anna and Neidhardt, Julia and Wissik, Tanja},
  title =	{{Exploring Causal Relationships Among Emotional and Topical Trajectories in Political Text Data}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{38:1--38:8},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.38},
  URN =		{urn:nbn:de:0030-drops-145740},
  doi =		{10.4230/OASIcs.LDK.2021.38},
  annote =	{Keywords: time-series clustering, Granger causality, topical stability, emotion, political discourse}
}
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