Sentences with negative actors: negative strength quantified

Files: data1.xml,data3.xml,data3.xml (3 annotators) - XML validiert - 439 sentences - target: a negative cause (an actor etc.) represented by the Lemma - id: sentence number - string: the plain sentence - strength: negativity strength of the target - labels 0-3 - 0 no negative entity found (or parsi...

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Main Author: Manfred Klenner
Format: Other/Unknown Material
Language:German
Published: Zenodo 2023
Subjects:
Online Access:https://doi.org/10.5281/zenodo.7589628
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spelling ftzenodo:oai:zenodo.org:7589628 2024-09-15T18:14:22+00:00 Sentences with negative actors: negative strength quantified Manfred Klenner 2023-01-31 https://doi.org/10.5281/zenodo.7589628 deu ger Zenodo https://doi.org/10.5281/zenodo.7589627 https://doi.org/10.5281/zenodo.7589628 oai:zenodo.org:7589628 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode polarity strength negative polarity info:eu-repo/semantics/other 2023 ftzenodo https://doi.org/10.5281/zenodo.758962810.5281/zenodo.7589627 2024-07-27T04:25:43Z Files: data1.xml,data3.xml,data3.xml (3 annotators) - XML validiert - 439 sentences - target: a negative cause (an actor etc.) represented by the Lemma - id: sentence number - string: the plain sentence - strength: negativity strength of the target - labels 0-3 - 0 no negative entity found (or parsing error) - 1 slightly negative, 2 negative, 3 stronly negative - 115 out of 439 sentences with tag 0: i.e. sentences do not contain a negative actor - different reasons (see the paper below): modal, future tense etc. but also parsing errors Data source: Facebook posts of the AfD, a German right-wing party Examples: no actor here: passive voice <sent> 1 Junge 0 "Verletzt wurde auch ein 11-jähriger Junge . " </sent> stronly negative: <sent> 411 Euro 3 "Der Euro ruiniert Europa . " </sent> negative: <sent> 214 Merkel 2 "Merkel verantwortet zusätzliche 50 Milliarden Sozialkosten bis 2018 . " </sent> slightly negative: <sent> 154 Meuthen 1 "Meuthen schadet der Partei . " </sent> References: @inproceedings{nodalida, month = {Juni}, author = {Manfred Klenner and Anne G{\"o}hring and Sophia Conrad}, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)}, address = {Reykjavik, Iceland}, title = {Getting Hold of Villains and other Rogues}, publisher = {Virtual Event}, pages = {435--439}, year = {2021}, language = {english}, url = {https://doi.org/10.5167/uzh-204265}, abstract = {In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.} } Other/Unknown Material Iceland Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language German
topic polarity strength
negative polarity
spellingShingle polarity strength
negative polarity
Manfred Klenner
Sentences with negative actors: negative strength quantified
topic_facet polarity strength
negative polarity
description Files: data1.xml,data3.xml,data3.xml (3 annotators) - XML validiert - 439 sentences - target: a negative cause (an actor etc.) represented by the Lemma - id: sentence number - string: the plain sentence - strength: negativity strength of the target - labels 0-3 - 0 no negative entity found (or parsing error) - 1 slightly negative, 2 negative, 3 stronly negative - 115 out of 439 sentences with tag 0: i.e. sentences do not contain a negative actor - different reasons (see the paper below): modal, future tense etc. but also parsing errors Data source: Facebook posts of the AfD, a German right-wing party Examples: no actor here: passive voice <sent> 1 Junge 0 "Verletzt wurde auch ein 11-jähriger Junge . " </sent> stronly negative: <sent> 411 Euro 3 "Der Euro ruiniert Europa . " </sent> negative: <sent> 214 Merkel 2 "Merkel verantwortet zusätzliche 50 Milliarden Sozialkosten bis 2018 . " </sent> slightly negative: <sent> 154 Meuthen 1 "Meuthen schadet der Partei . " </sent> References: @inproceedings{nodalida, month = {Juni}, author = {Manfred Klenner and Anne G{\"o}hring and Sophia Conrad}, booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)}, address = {Reykjavik, Iceland}, title = {Getting Hold of Villains and other Rogues}, publisher = {Virtual Event}, pages = {435--439}, year = {2021}, language = {english}, url = {https://doi.org/10.5167/uzh-204265}, abstract = {In this paper, we introduce the first corpus specifying negative entities within sentences. We discuss indicators for their presence, namely particular verbs, but also the linguistic conditions when their prediction should be suppressed. We further show that a fine-tuned Bert-based baseline model outperforms an over-generating rule-based approach which is not aware of these further restrictions. If a perfect filter were applied, both would be on par.} }
format Other/Unknown Material
author Manfred Klenner
author_facet Manfred Klenner
author_sort Manfred Klenner
title Sentences with negative actors: negative strength quantified
title_short Sentences with negative actors: negative strength quantified
title_full Sentences with negative actors: negative strength quantified
title_fullStr Sentences with negative actors: negative strength quantified
title_full_unstemmed Sentences with negative actors: negative strength quantified
title_sort sentences with negative actors: negative strength quantified
publisher Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.7589628
genre Iceland
genre_facet Iceland
op_relation https://doi.org/10.5281/zenodo.7589627
https://doi.org/10.5281/zenodo.7589628
oai:zenodo.org:7589628
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.758962810.5281/zenodo.7589627
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