SOTAVerified

Annotations for Exploring Food Tweets From Multiple Aspects

2024-12-09Code Available0· sign in to hype

Matīss Rikters, Edison Marrese-Taylor, Rinalds Vīksna

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supplement the LTEC with manually annotated subsets of evaluation data for machine translation, named entity recognition, timeline-balanced sentiment analysis, and text-image relation classification. We experiment with each of the data sets using baseline models and highlight future challenges for various modelling approaches.

Tasks

Reproductions