GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception

TL;DR

This paper introduces a new annotated corpus of 5000 news headlines with detailed emotion, semantic roles, and reader perception annotations, enabling advanced emotion analysis research.

Contribution

It provides a large, richly annotated dataset for emotion and semantic role analysis in news headlines, along with a baseline for automatic role prediction.

Findings

Successful annotation of complex emotion and semantic roles

Baseline model for automatic semantic role prediction

Dataset supports diverse emotion analysis tasks

Abstract

Most research on emotion analysis from text focuses on the task of emotion classification or emotion intensity regression. Fewer works address emotions as a phenomenon to be tackled with structured learning, which can be explained by the lack of relevant datasets. We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, related emotion causes and targets, as well as the reader's perception of the emotion of the headline. This annotation task is comparably challenging, given the large number of classes and roles to be identified. We therefore propose a multiphase annotation procedure in which we first find relevant instances with emotional content and then annotate the more fine-grained aspects. Finally, we develop a baseline for the task of automatic prediction of semantic role structures and discuss the results. The corpus we release enables further research on emotion classification, emotion intensity prediction, emotion cause detection, and supports further qualitative studies.