README.md

---
license: mit
task_categories:
- text-classification
task_ids:
- sentiment-classification
language:
- lv
tags:
- sentiment
- sentiment analysis
- sentiment classification
- Latvian
- Twitter
- social media
- short text
pretty_name: Latvian Twitter Eater Corpus - Sentiment
size_categories:
- 1K<n<10K
dataset_info:
  features:
    - name: label
      dtype:
        class_label:
          names:
            '0': neu
            '1': pos
            '2': neg
    - name: text
      dtype: string
    - name: screen_name
      dtype: string
    - name: tweet_id
      dtype: int64
---





# Latvian Twitter Eater Corpus - Sentiment Analysis Sub-corpus

This data set contains 5420 tweets with human-annotated sentiment as positive (pos), neutral (neu) or negative (neg). 1631 tweets are positive, 2507 - neutral and 1282 - negative.

- **ltec-sentiment-annotated-train.json** contains tweets with human annotated sentiment
- **ltec-sentiment-annotated-test.json** contains the test set that we used in our paper

## Tweet Structure
```json
{   
	"label":1,
	"screen_name":"artisare",
	"tweet_id":221520985738846209,
	"text":"@mazheks Burgā ir brančs?!? Es jau sāku domāt ka uz Pērli jāmauc ēst pirms tam Illy paķerot kafiju. Cikos domā?"
}
```


## Other Latvian Twitter sentiment corpora
---------
* [Pinnis](https://github.com/pmarcis/latvian-tweet-corpus) - ~ 7000 tweets from politicians and companies
* [Peisenieks](https://github.com/FnTm/latvian-tweet-sentiment-corpus) - ~ 1000 general tweets with sentiment annotated by multiple annotators
* [Vīksna](https://github.com/RinaldsViksna/sikzinu_analize) - ~ 4000 general tweets
* [Nicemanis](https://github.com/nicemanis/LV-twitter-sentiment-corpus) - ~ 2000 general tweets
* [Špats](https://github.com/gatis/om) - ~ 6000 general tweets


Publications
---------

If you use this corpus or scripts, please cite the following paper:

Uga Sproģis and Matīss Rikters (2020). "[What Can We Learn From Almost a Decade of Food Tweets.](https://arxiv.org/abs/2007.05194)" In Proceedings of the 9th Conference Human Language Technologies - The Baltic Perspective ([Baltic HLT 2020](https://klc.vdu.lt/hlt/programme)) (2020).

```bibtex
@inproceedings{SprogisRikters2020BalticHLT,
	author = {Sproģis, Uga and Rikters, Matīss},
	booktitle={In Proceedings of the 9th Conference Human Language Technologies - The Baltic Perspective (Baltic HLT 2020)},
	title = {{What Can We Learn From Almost a Decade of Food Tweets}},
	address={Kaunas, Lithuania},
	year = {2020}
}
```