Beyond sentiment: an algorithmic strategy for identifying evaluations within large text corpora.

ABSTRACT: In this paper, we propose a new strategy for classifying evaluations in large text corpora, using supervised machine learning (SML). Departing from a conceptual and methodological critique of the use of sentiment measures to recognize object-specific evaluations, we argue that a key challenge consists in determining whether a semantic relationship exists between evaluative expressions and evaluated objects. Regarding sentiment terms as merely potentially evaluative expressions, we thus use a SML classifier to decide whether recognized terms have an evaluative function in relation to the evaluated object. We train and test our classifier on a corpus of 10,004 segments of election coverage from 16 major U.S. news outlets and Tweets by 10 prominent U.S. politicians and journalists. Spe

SUBMITTER: Overbeck M 

PROVIDER: S-EPMC11854042 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature