Subjective texts have been 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.

本文通过针对 2019 年阿根廷总统大选主要媒体发布的 1,976 个题名候选人的情感数据集，利用预训练的语言模型和最先进的分类算法，解决了特定领域新闻标题情感分析的问题，并公开了数据和模型。