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Methodological Challenges in Estimating Tone: Application to News Coverage of the U.S. Economy

Sat, May 27, 8:00 to 9:15, Hilton San Diego Bayfront, 3, Aqua 313

Abstract

Automated text analysis methods have made possible the classification of large corpora of text by measures such as topic, tone, and ideology. However, even when using dictionary-based methods that require few inputs, many decisions must be made before a measure of any kind is produced from the text. When coding media, the analyst must decide on the universe of sources to sample from, as well as the criteria for selecting articles. If utilizing a supervised learning method, the method of generating training data presents many decisions: the unit of analysis, choice of coders, number of units to code, number of coders per unit, etc. In this paper we consider the choices that automated analysis of media text involves -- using as a running example efforts to measure the tone of newspaper coverage of the economy -- and highlight our key findings throughout.

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