fit_term_usage

Internal stylest2 function to compute term usage

internal

Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) <doi:10.1017/pan.2019.49>. The model is built on a Bayesian framework in which the distinctiveness of each speaker is defined by how different, on average, the speaker's terms are to everyone else in the corpus of texts. An optional cross-validation method is implemented to select the subset of terms that generate the most accurate speaker predictions. Once a set of terms is selected, the model can be estimated. Speaker distinctiveness and term influence can be recovered from parameters in the model using package functions. Once fitted, the model can be used to predict authorship of new texts.

Christian Baehr

stylest2

Estimating Speakers of Texts

Arthur Spirling

Leslie Huang

fit_term_usage function

<dl><dt>dfm</dt>
<dd>a quanteda <code>dfm</code> object</dd>
<dt>smoothing</dt>
<dd>the smoothing parameter value for smoothing the dfm. Should 
be a numeric scalar, default to 0.5.</dd>
<dt>terms</dt>
<dd>If not <code>NULL</code>, terms to be used in the model. If <code>NULL</code>, 
use all terms.</dd>
<dt>term_weights</dt>
<dd>Named vector of distances (or any weights) per term in the 
vocab. Names should correspond to the term.</dd></dl>

Arguments

Internal stylest2 function to compute term usage — fit_term_usage

<dl>

<dt>dfm</dt>
<dd>a quanteda <code>dfm</code> object</dd>


<dt>smoothing</dt>
<dd>the smoothing parameter value for smoothing the dfm. Should 
be a numeric scalar, default to 0.5.</dd>


<dt>terms</dt>
<dd>If not <code>NULL</code>, terms to be used in the model. If <code>NULL</code>, 
use all terms.</dd>


<dt>term_weights</dt>
<dd>Named vector of distances (or any weights) per term in the 
vocab. Names should correspond to the term.</dd>

</dl>

fit_term_usage: Internal stylest2 function to compute term usage

Description

Usage

Value

Arguments