  Sentiment analysis is one of the most widely used techniques in text
analysis. 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 in that it entails multiple variables, such as
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 sentiment labels most strongly
correlate with valence labels. I further find that classification improves when
researchers more precisely specify their dimension of interest rather than
using the less well-defined concept of sentiment. I conclude by encouraging
researchers to move beyond "sentiment" when feasible and use a more precise
measurement construct.
