A Spanish dataset for Targeted Sentiment Analysis of political headlines
DOI:
https://doi.org/10.24215/15146774e004Abstract
Subjective texts have been extensively studied due to their potential to influence behaviors. While most research has focused on user-generated texts in social networks, other types of texts, such as news headlines expressing opinions on certain topics, can also influence judgment criteria during political decisions. In this paper, we address the task of Targeted Sentiment Analysis for news headlines related to the 2019 Argentinean Presidential Elections, published by major news outlets. To facilitate research in this area, we present a polarity dataset comprising 1,976 headlines that mention candidates at the target level. Our experiments using state-of-the-art classification algorithms based on pre-trained language models demonstrate the usefulness of target information for this task. We also provide public access to our data and models to foster further research.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Juan Manuel Pérez, Emilio Recart Zapata, Tomás Alves Salgueiro, Damián Furman, Pablo Nicolás Fernández Larrosa
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Those authors who have publications with this journal, agree with the following terms:
a. Authors will retain its copyright and will ensure the rights of first publication of its work to the journal, which will be at the same time subject to the Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) allowing third parties to share the work as long as the author and the first publication on this journal is indicated.
b. Authors may elect other non-exclusive license agreements of the distribution of the published work (for example: locate it on an institutional telematics file or publish it on an monographic volume) as long as the first publication on this journal is indicated,
c. Authors are allowed and suggested to disseminate its work through the internet (for example: in institutional telematics files or in their website) before and during the submission process, which could produce interesting exchanges and increase the references of the published work. (see The effect of open Access)
