Do female legislators have different policy preferences than male legislators? Despite a large body of literature from liberal democracies and recent studies of electoral authoritarian regimes, this topic has received little attention in the context of single-party regimes. Based on quasi-experimental methods and regression models, we analyze original data from 38,383 proposals introduced during China’s 12th National People’s Congress and test the effect of gender on policies concerning conventionally selected feminine issues and “political stance,” issues that are unique to single-party regimes. The analysis confirms the effect of gender on policy preference across several feminine issues. However, the effect of gender is null on issues concerning political stance. Our findings suggest that while single-party regimes allow gender differences to emerge among legislators on issues that are not politically important, they tend to discourage such differences on politically prominent issues. This study advances the literature on both gender politics and authoritarian politics.

Sentiment analysis techniques have a long history in natural language processing and have become a standard tool in the analysis of political texts, promising a conceptually straightforward automated method of extracting meaning from textual data by scoring documents on a scale from positive to negative. However, while these kinds of sentiment scores can capture the overall tone of a document, the underlying concept of interest for political analysis is often actually the document’s stance with respect to a given target—how positively or negatively it frames a specific idea, individual, or group—as this reflects the author’s underlying political attitudes. In this paper, we question the validity of approximating author stance through sentiment scoring in the analysis of political texts, and advocate for greater attention to be paid to the conceptual distinction between a document’s sentiment and its stance. Using examples from open-ended survey responses and from political discussions on social media, we demonstrate that in many political text analysis applications, sentiment and stance do not necessarily align, and therefore sentiment analysis methods fail to reliably capture ground-truth document stance, amplifying noise in the data and leading to faulty conclusions.