Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models.

ABSTRACT: This work describes a chronological (2000-2019) analysis of sentiment and emotion in 23 million headlines from 47 news media outlets popular in the United States. We use Transformer language models fine-tuned for detection of sentiment (positive, negative) and Ekman's six basic emotions (anger, disgust, fear, joy, sadness, surprise) plus neutral to automatically label the headlines. Results show an increase of sentiment negativity in headlines across written news media since the year 2000. Headlines from right-leaning news media have been, on average, consistently more negative than headlines from left-leaning outlets over the entire studied time period. The chronological analysis of headlines emotionality shows a growing proportion of headlines denoting anger, fear, disgust and sadness an

SUBMITTER: Rozado D 

PROVIDER: S-EPMC9578611 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature