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BioMAISx: A Corpus for Aspect-Based Sentiment Analysis of Media Representations of Agricultural Biotechnologies in Africa

  • Patricia Chiril
  • , Trevor Spreadbury
  • , Joeva Sean Rock
  • , Brian Dowd-Uribe
  • , David Uminsky
  • The University of Chicago
  • University of San Francisco

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

News articles constitute a valuable resource for opinion mining, as they contain important perspectives related to the subject matter they cover. In this paper, we explore how aspect-based sentiment analysis might help in understanding the public discourse surrounding agricultural biotechnologies in Africa. We introduce BioMAISx, the first English language dataset composed of direct quotes pertaining to agricultural biotechnologies extracted from a curated list of Africa-based news sources. We have identified and labelled entities related to key aspects of agricultural biotechnologies, providing valuable insights into public discourse. This dataset can aid in identifying challenges, improving public discourse, and monitoring the perception of agricultural biotechnologies, thus contributing to informed decision-making.

Original languageEnglish
Title of host publicationCIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages5338-5342
Number of pages5
ISBN (Electronic)9798400704369
DOIs
StatePublished - Oct 21 2024
Event33rd ACM International Conference on Information and Knowledge Management, CIKM 2024 - Boise, United States
Duration: Oct 21 2024Oct 25 2024

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
ISSN (Print)2155-0751

Conference

Conference33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Country/TerritoryUnited States
CityBoise
Period10/21/2410/25/24

Keywords

  • Africa
  • aspect-based sentiment analysis
  • corpus
  • genetically modified crops
  • media analysis
  • new breeding technologies

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