Mind(d)ing the President: A text analytic approach to measuring tax news
<|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:'This data set contains supplementary materials for 'Mind(d)ing the President: A text analytic approach to measuring tax news'.It consists of the estimation results of the topic model and the estimated tax news measures.Folder LDA contains data and code for replicating the topic modelling results and figures.Folder TaxNews contains data and code for replicating the econometric analysis and figures' . 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" : ["fiscal foresight","identification","Latent Dirichlet Allocation","news","tax shocks","text mining","topic model"]} <|eot_id|>
 Here is the Description of a dataset. Description:'This data set contains supplementary materials for 'Mind(d)ing the President: A text analytic approach to measuring tax news'.It consists of the estimation results of the topic model and the estimated tax news measures.Folder LDA contains data and code for replicating the topic modelling results and figures.Folder TaxNews contains data and code for replicating the econometric analysis and figures' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
This data set contains supplementary materials for 'Mind(d)ing the President: A text analytic approach to measuring tax news'.It consists of the estimation results of the topic model and the estimated tax news measures.Folder LDA contains data and code for replicating the topic modelling results and figures.Folder TaxNews contains data and code for replicating the econometric analysis and figures
fiscal foresight; identification; Latent Dirichlet Allocation; news; tax shocks; text mining; topic model