Replication Material for 'How Populist Are Parties? Measuring Degrees of Populism in Party Manifestos Using Supervised Machine Learning'
<|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:'One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information of temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the pa' . 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" : ["Populism, textual analysis, text-as-data, political parties, computational politics"]} <|eot_id|>
 Here is the Description of a dataset. Description:'One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information of temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the pa' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
One of the main challenges in comparative studies on populism concerns its temporal and spatial measurements within and between a large number of parties and countries. Textual analysis has proved useful for these purposes, and automated methods can further improve research in this direction. Here we propose a method to derive a score of parties’ levels of populism using supervised machine learning to perform textual analysis on national manifestos. We illustrate the advantages of our approach, which allows for measuring populism for a vast number of parties and countries without resource-intensive human-coding processes and provides accurate, updated information of temporal and spatial comparisons of populism. Furthermore, our method allows for obtaining a continuous score of populism, which ensures more fine-grained analyses of the pa
Populism, textual analysis, text-as-data, political parties, computational politics