A Spanish dataset for Targeted Sentiment Analysis of political headlines

• URL: http://arxiv.org/abs/2208.13947v1
• Date: Tue, 30 Aug 2022 01:30:30 GMT
• Title: A Spanish dataset for Targeted Sentiment Analysis of political headlines
• Authors: Tom\'as Alves Salgueiro, Emilio Recart Zapata, Dami\'an Furman, Juan Manuel P\'erez, Pablo Nicol\'as Fern\'andez Larrosa
• Abstract summary: This work addresses the task of Targeted Sentiment Analysis for the domain of news headlines, published by the main outlets during the 2019 Argentinean Presidential Elections. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• Classifying Human-Generated and AI-Generated Election Claims in Social Media [8.990994727335064]
  Malicious actors may use social media to disseminate misinformation to undermine trust in the electoral process. The emergence of Large Language Models (LLMs) exacerbates this issue by enabling malicious actors to generate misinformation at an unprecedented scale. We present a novel taxonomy for characterizing election-related claims.
  arXiv  Detail & Related papers  (2024-04-24T18:13:29Z)
• When is Off-Policy Evaluation (Reward Modeling) Useful in Contextual Bandits? A Data-Centric Perspective [64.73162159837956]
  evaluating the value of a hypothetical target policy with only a logged dataset is important but challenging. We propose DataCOPE, a data-centric framework for evaluating a target policy given a dataset. Our empirical analysis of DataCOPE in the logged contextual bandit settings using healthcare datasets confirms its ability to evaluate both machine-learning and human expert policies.
  arXiv  Detail & Related papers  (2023-11-23T17:13:37Z)
• Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines [3.3228144010758593]
  IEHate dataset contains 11,457 manually annotated Hindi tweets related to the Indian Assembly Election Campaign from November 1, 2021, to March 9, 2022. We benchmark the dataset using a range of machine learning, deep learning, and transformer-based algorithms. In particular, the relatively higher score of human evaluation over algorithms emphasizes the importance of utilizing both human and automated approaches for effective hate speech moderation.
  arXiv  Detail & Related papers  (2023-06-26T15:17:54Z)
• How to Solve Few-Shot Abusive Content Detection Using the Data We Actually Have [58.23138483086277]
  In this work we leverage datasets we already have, covering a wide range of tasks related to abusive language detection. Our goal is to build models cheaply for a new target label set and/or language, using only a few training examples of the target domain. Our experiments show that using already existing datasets and only a few-shots of the target task the performance of models improve both monolingually and across languages.
  arXiv  Detail & Related papers  (2023-05-23T14:04:12Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News Text [4.218255132083181]
  We propose a novel NLP task of identifying directed sentiment relationship between political entities from a given news document. From a million-scale news corpus, we construct a dataset of news sentences where sentiment relations of political entities are manually annotated. We demonstrate the utility of our proposed method for social science research questions by analyzing positive and negative opinions between political entities in two major events: 2016 U.S. presidential election and COVID-19.
  arXiv  Detail & Related papers  (2021-06-02T09:02:14Z)
• PolicyQA: A Reading Comprehension Dataset for Privacy Policies [77.79102359580702]
  We present PolicyQA, a dataset that contains 25,017 reading comprehension style examples curated from an existing corpus of 115 website privacy policies. We evaluate two existing neural QA models and perform rigorous analysis to reveal the advantages and challenges offered by PolicyQA.
  arXiv  Detail & Related papers  (2020-10-06T09:04:58Z)
• A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
  The last decade has seen a surge of research in this area due to the unprecedented success of deep learning. This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021. We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
  arXiv  Detail & Related papers  (2020-08-02T00:09:03Z)
• Inferring Political Preferences from Twitter [0.0]
  Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate. During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians. In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning.
  arXiv  Detail & Related papers  (2020-07-21T05:20:43Z)
• Political Advertising Dataset: the use case of the Polish 2020 Presidential Elections [4.560033258611709]
  We present the first publicly open dataset for detecting specific text chunks and categories of political advertising in the Polish language. It contains 1,705 human-annotated tweets tagged with nine categories, which constitute campaigning under Polish electoral law.
  arXiv  Detail & Related papers  (2020-06-17T23:58:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.