AUTNES

AUTNES - Austrian National Election Study is engaged in the comprehensive social science analysis of Austrian national elections. AUTNES is part of the Vienna Center for Electoral Research (VieCER).

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291 to 300 of 388 Results

10724_co_en_v1_0.pdf Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Adobe PDF - 307.6 KB - MD5: 13a8234eba6443709a0ef21f8213d071 CodebookDocumentation Codebook
10724_da_en_v1_0.tab Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Tabular Data - 29.7 MB - 33 Variables, 77573 Observations - UNF:6:Y6Z5/MsaStVMBoiSBddTAA== DataSTATA Core data file, STATA format - 33 Variables, 77573 Observations
10724_da_en_v1_0.zsav Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Unknown - 10.4 MB - MD5: 20dd6d0e8385775f2e14eb8806933400 DataSPSS Core data file - compressed SPSS format - 33 Variables, 77573 Observations
10724_mr_en_v1_0.pdf Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Adobe PDF - 774.1 KB - MD5: b813de2b5968c827a30c4f8d54779ae8 DocumentationMethod report Method report
10724_vi_en_v1_0.tab Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition) Tabular Data - 445.1 KB - 3 Variables, 11911 Observations - UNF:6:yjFhsObnuv4OUh7FcwKMng== Documentation Variable identifiers and descriptions - machine-readable
The German Political Sentiment Dictionary (SUF edition) Dec 2, 2020 Haselmayer, Martin; Jenny, Marcelo, 2020, "The German Political Sentiment Dictionary (SUF edition)", https://doi.org/10.11587/7PFLIU, AUSSDA, V1, UNF:6:fddvmzxSkM88VgrqvxOqTg== [fileUNF] Full edition for scientific use. This dataset contains a German-language sentiment dictionary of 5001 negative words and their associated sentiment strength on a five-point-scale from 0 (not negative) to 4 (very strongly negative). The procedure for building the dictionary contains the following steps: (1) Sampling 12713 sentences from Austrian par...
10274_da_de_v1_0.tab Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition) Tabular Data - 399.2 KB - 8 Variables, 5001 Observations - UNF:6:fddvmzxSkM88VgrqvxOqTg== DataSTATA Core data file - STATA format - 8 Variables, 5001 Observations
10274_mr_en_v1_0.pdf Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition) Adobe PDF - 62.9 KB - MD5: 050d20c9d45e62745c30f748be8cb9ed DocumentationMethod report Method report
Training Data for German Sentiment Analysis of Political Communication (SUF edition) Nov 26, 2020 Haselmayer, Martin; Jenny, Marcelo, 2020, "Training Data for German Sentiment Analysis of Political Communication (SUF edition)", https://doi.org/10.11587/EOPCOB, AUSSDA, V1, UNF:6:wx/QlNpI73mL4r3N+cNhBg== [fileUNF] Full edition for scientific use. The dataset contains 125871 sentences extracted from Austrian parliamentary debates and party press releases. Press releases were collected under the auspices of the Austrian National Election Study (AUTNES) and cover 6 weeks prior to each national election 1995-2013. Data from parliamentary debates stem from a rand...
10273_da_de_v1_0.tab Nov 26, 2020 - Training Data for German Sentiment Analysis of Political Communication (SUF edition) Tabular Data - 22.3 MB - 7 Variables, 125871 Observations - UNF:6:IiVRvJlBGz0ZxTi2febxHA== DataSTATA Core data file - STATA format - 7 Variables, 125871 Observations

10724_co_en_v1_0.pdf

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Adobe PDF - 307.6 KB -

CodebookDocumentation

Codebook

10724_da_en_v1_0.tab

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Tabular Data - 29.7 MB - 33 Variables, 77573 Observations -

DataSTATA

Core data file, STATA format - 33 Variables, 77573 Observations

10724_da_en_v1_0.zsav

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Unknown - 10.4 MB -

DataSPSS

Core data file - compressed SPSS format - 33 Variables, 77573 Observations

10724_mr_en_v1_0.pdf

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Adobe PDF - 774.1 KB -

DocumentationMethod report

Method report

10724_vi_en_v1_0.tab

Dec 17, 2020 - AUTNES Content Analysis of Party Manifestos: Cumulative File (SUF edition)

Tabular Data - 445.1 KB - 3 Variables, 11911 Observations -

Documentation

Variable identifiers and descriptions - machine-readable

The German Political Sentiment Dictionary (SUF edition)

Dec 2, 2020

Haselmayer, Martin; Jenny, Marcelo, 2020, "The German Political Sentiment Dictionary (SUF edition)", https://doi.org/10.11587/7PFLIU, AUSSDA, V1, UNF:6:fddvmzxSkM88VgrqvxOqTg== [fileUNF]

Full edition for scientific use. This dataset contains a German-language sentiment dictionary of 5001 negative words and their associated sentiment strength on a five-point-scale from 0 (not negative) to 4 (very strongly negative). The procedure for building the dictionary contains the following steps: (1) Sampling 12713 sentences from Austrian par...

10274_da_de_v1_0.tab

Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition)

Tabular Data - 399.2 KB - 8 Variables, 5001 Observations -

DataSTATA

Core data file - STATA format - 8 Variables, 5001 Observations

10274_mr_en_v1_0.pdf

Dec 2, 2020 - The German Political Sentiment Dictionary (SUF edition)

Adobe PDF - 62.9 KB -

DocumentationMethod report

Method report

Training Data for German Sentiment Analysis of Political Communication (SUF edition)

Nov 26, 2020

Haselmayer, Martin; Jenny, Marcelo, 2020, "Training Data for German Sentiment Analysis of Political Communication (SUF edition)", https://doi.org/10.11587/EOPCOB, AUSSDA, V1, UNF:6:wx/QlNpI73mL4r3N+cNhBg== [fileUNF]

Full edition for scientific use. The dataset contains 125871 sentences extracted from Austrian parliamentary debates and party press releases. Press releases were collected under the auspices of the Austrian National Election Study (AUTNES) and cover 6 weeks prior to each national election 1995-2013. Data from parliamentary debates stem from a rand...

10273_da_de_v1_0.tab

Nov 26, 2020 - Training Data for German Sentiment Analysis of Political Communication (SUF edition)

Tabular Data - 22.3 MB - 7 Variables, 125871 Observations -

DataSTATA

Core data file - STATA format - 7 Variables, 125871 Observations

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