Replication Data for: Estimating Ideal Points of Newspapers from Editorial Texts
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Although measuring the ideal points of news media is essential for testing political communication theories based on spatial theory, prior methods of estimating ideal points of media outlets have various shortcomings, including high cost in terms of time and human resources and low applicability to different countries. We propose that unsupervised machine learning techniques for text data, specifically the combination of a text scaling method and latent topic modeling, can be applied to estimate ideal points of media outlets. We applied our proposed methods to editorial texts of ten national and regional newspapers in Japan, where prior approaches are not applicable because newspapers have never officially endorsed particular parties or candidates, and because high-quality training data for supervised learning is not available. Our two ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["ideal point estimation","Japan","media slant","quantitative text analysis","unsupervised learning"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Although measuring the ideal points of news media is essential for testing political communication theories based on spatial theory, prior methods of estimating ideal points of media outlets have various shortcomings, including high cost in terms of time and human resources and low applicability to different countries. We propose that unsupervised machine learning techniques for text data, specifically the combination of a text scaling method and latent topic modeling, can be applied to estimate ideal points of media outlets. We applied our proposed methods to editorial texts of ten national and regional newspapers in Japan, where prior approaches are not applicable because newspapers have never officially endorsed particular parties or candidates, and because high-quality training data for supervised learning is not available. Our two ' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Although measuring the ideal points of news media is essential for testing political communication theories based on spatial theory, prior methods of estimating ideal points of media outlets have various shortcomings, including high cost in terms of time and human resources and low applicability to different countries. We propose that unsupervised machine learning techniques for text data, specifically the combination of a text scaling method and latent topic modeling, can be applied to estimate ideal points of media outlets. We applied our proposed methods to editorial texts of ten national and regional newspapers in Japan, where prior approaches are not applicable because newspapers have never officially endorsed particular parties or candidates, and because high-quality training data for supervised learning is not available. Our two 
ideal point estimation; Japan; media slant; quantitative text analysis; unsupervised learning