Sentiment is Not Stance: Target-Aware Opinion Classification for Political Text Analysis

Abstract 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.