hypothesis; particularly when coupled with domain knowledge of row and column dependencies in $U$.

The 55 most highly weighted features for each label are reported in the Appendix. Though the word lists are shown in order of importance, this ranking is not strict. Different regularization, preprocessing, or train/test splits can alter the ordering, especially when there are few examples. Additionally, more common words with lower weights may be used more often in a model’s prediction, but may not appear at the top of a list. One may use $\ell_1$ regularization to obtain an arbitrary small number of non-zero weights [@meinshausen2009lasso]. This encourages weighting common words and provides more stable rankings. We demonstrate that approach with our IQ model in Section \[IQ\].

There are many well-studied phenomena embedded in the $\hat{U}$ produced by our work. For example, Sarah Palin is the only politician indicated in the liberal word list in Table \[words\_rel\_pol\]. Likewise, Nancy Pelosi ranks just below Ronald Reagan among conservative words. This accords with literature on the memorability of negative ads [@lau2007effects], importance of outgroup prejudice for social identity [@huddy2003group; @branscombe1994collective], and biases women face in politics [@schneider2014measuring; @dolan2010impact]. We hope the many word lists in the appendix will 