A Spanish dataset for Targeted Sentiment Analysis of political headlines

TOMÁS ALVES SALGUEIRO; EMILIO RECART ZAPATA; DAMIÁN FURMAN; JUAN MANUEL PÉREZ; PABLO NICOLÁS FERNÁNDEZ LARROSA

Buenos Aires

Simposio; ASAI 2022 - Simposio Argentino de Inteligencia Artificial; 2022

Subjective texts have been especially studied by several works as they can induce certain behaviours in their users. Most work focuses on user-generated texts in social networks, but some other texts also comprise opinions on  certain topics and could influence judgement criteria during political decisions. In this work, we address the task of Targeted Sentiment Analysis for the domain of news headlines, published by the main outlets during the 2019 Argentinean Presidential Elections. For this purpose, we present a polarity dataset of 1,976 headlines mentioning candidates in the 2019 elections at the target level. Preliminary experiments with state-of-the-art classification algorithms based on pre-trained linguistic models suggest that target information is helpful for this task. We make our data and pre-trained models publicly available.