What is sentiment meant to mean to language models?

Sentiment analysis is one of the most widely used text analysis methods in social science. Recent advancements with large language models have made it more accurate and accessible than ever, allowing researchers to classify text with only a plain English prompt. However, “sentiment” entails a wide variety of concepts depending on the domain and tools used. It has been used to mean emotion, opinions, market movements, or simply a general “good-bad” dimension. This raises a question: What exactly are language models doing when prompted to label documents by sentiment? This paper first overviews how sentiment is defined across different contexts, highlighting that it is a confounded measurement construct that entails multiple concepts, particularly emotional valence and opinion, without disentangling them. I then test three language models across two data sets with prompts requesting sentiment, valence, and stance classification. I find that language models generally understand sentiment in terms of emotional valence rather than opinion. I find that classification generally improves when researchers more precisely specify their dimension, but that even when classification metrics are similar the different prompts are biased in different ways. I conclude by encouraging researchers to move beyond sentiment when feasible and use more precise measurement constructs.