Annotations for Exploring Food Tweets From Multiple Aspects

TL;DR

This paper enhances the Latvian Twitter Eater Corpus with manually annotated datasets for multiple NLP tasks, enabling comprehensive analysis of food-related tweets and highlighting future research challenges.

Contribution

It introduces manually annotated subsets for evaluation in machine translation, NER, sentiment analysis, and text-image relation classification within a large food-related Twitter corpus.

Findings

Baseline model experiments conducted on each dataset.

Identification of future challenges in modeling food-related social media data.

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.