# What Can We Learn From Almost a Decade of Food Tweets

arXiv: https://arxiv.org/abs/2007.05194 · HTML: https://mlsanity.com/paper/2007.05194/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [Massively Multilingual Corpus of Sentiment Datasets and Multi-faceted Sentiment Classification Benchmark](https://mlsanity.com/paper/2306.07902/) (2023)
- [GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph Knowledge](https://mlsanity.com/paper/2409.18193/) (2024)
- [Assessment of Massively Multilingual Sentiment Classifiers](https://mlsanity.com/paper/2204.04937/) (2022)
- [Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages](https://mlsanity.com/paper/2502.10140/) (2025)
- [Annotations for Exploring Food Tweets From Multiple Aspects](https://mlsanity.com/paper/2412.06179/) (2024)
- [Fragmented and Valuable: Following Sentiment Changes in Food Tweets](https://mlsanity.com/paper/2106.04903/) (2021)
- [What Food Do We Tweet about on a Rainy Day?](https://mlsanity.com/paper/2304.05041/) (2023)
- [Pretraining and Benchmarking Modern Encoders for Latvian](https://mlsanity.com/paper/2603.15005/) (2026)

## Abstract (arXiv)

We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking. The corpus has been collected over time-span of over 8 years and includes over 2 million tweets entailed with additional useful data. We also separate two sub-corpora of question and answer tweets and sentiment annotated tweets. We analyse contents of the corpus and demonstrate use-cases for the sub-corpora by training domain-specific question-answering and sentiment-analysis models using data from the corpus.