# Annotations for Exploring Food Tweets From Multiple Aspects

arXiv: https://arxiv.org/abs/2412.06179 · HTML: https://mlsanity.com/paper/2412.06179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 9 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [What Can We Learn From Almost a Decade of Food Tweets](https://mlsanity.com/paper/2007.05194/) (2020)
- [Pretraining and Fine-Tuning Strategies for Sentiment Analysis of Latvian Tweets](https://mlsanity.com/paper/2010.12401/) (2020)
- [What Food Do We Tweet about on a Rainy Day?](https://mlsanity.com/paper/2304.05041/) (2023)

## Built on by (most cited first, 2 of 2)

- [Image-Text Relation Prediction for Multilingual Tweets](https://mlsanity.com/paper/2505.05040/) (2025)
- [Pretraining and Benchmarking Modern Encoders for Latvian](https://mlsanity.com/paper/2603.15005/) (2026)

## Abstract (arXiv)

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.