{"title":"使用txtreg_train估计文本回归","authors":"Carlo Schwarz","doi":"10.1177/1536867x231196349","DOIUrl":null,"url":null,"abstract":"In this article, I introduce new commands to estimate text regressions for continuous, binary, and categorical variables based on text strings. The command txtreg_train automatically handles text cleaning, tokenization, model training, and cross-validation for lasso, ridge, elastic-net, and regularized logistic regressions. The txtreg_predict command obtains the predictions from the trained text regression model. Furthermore, the txtreg_analyze command facilitates the analysis of the coefficients of the text regression model. Together, these commands provide a convenient toolbox for researchers to train text regressions. They also allow sharing of pretrained text regression models with other researchers.","PeriodicalId":51171,"journal":{"name":"Stata Journal","volume":"368 1","pages":"0"},"PeriodicalIF":3.2000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating text regressions using txtreg_train\",\"authors\":\"Carlo Schwarz\",\"doi\":\"10.1177/1536867x231196349\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this article, I introduce new commands to estimate text regressions for continuous, binary, and categorical variables based on text strings. The command txtreg_train automatically handles text cleaning, tokenization, model training, and cross-validation for lasso, ridge, elastic-net, and regularized logistic regressions. The txtreg_predict command obtains the predictions from the trained text regression model. Furthermore, the txtreg_analyze command facilitates the analysis of the coefficients of the text regression model. Together, these commands provide a convenient toolbox for researchers to train text regressions. They also allow sharing of pretrained text regression models with other researchers.\",\"PeriodicalId\":51171,\"journal\":{\"name\":\"Stata Journal\",\"volume\":\"368 1\",\"pages\":\"0\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stata Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/1536867x231196349\",\"RegionNum\":2,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stata Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1536867x231196349","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
In this article, I introduce new commands to estimate text regressions for continuous, binary, and categorical variables based on text strings. The command txtreg_train automatically handles text cleaning, tokenization, model training, and cross-validation for lasso, ridge, elastic-net, and regularized logistic regressions. The txtreg_predict command obtains the predictions from the trained text regression model. Furthermore, the txtreg_analyze command facilitates the analysis of the coefficients of the text regression model. Together, these commands provide a convenient toolbox for researchers to train text regressions. They also allow sharing of pretrained text regression models with other researchers.
期刊介绍:
The Stata Journal is a quarterly publication containing articles about statistics, data analysis, teaching methods, and effective use of Stata''s language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.