Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
json
Sub-tasks:
sentiment-classification
Languages:
Latvian
Size:
1K - 10K
ArXiv:
License:
File size: 2,383 Bytes
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---
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
---
# Latvian Twitter Eater Corpus - Sentiment Analysis Sub-corpus
This sub-corpus 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.json** contains tweets with human annotated sentiment
- **ltec-sentiment-annotated-test.json** contains the test set that we used in our paper
- **ltec-sentiment-automatic.json** contains tweets with automatically assigned sentiment based on emoticons
## Tweet Structure
```json
{
"sentiment":"pos",
"screen_name":"artisare",
"tweet_id":221520985738846209,
"tweet_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
* [Nicmanis](https://github.com/nicemanis/LV-twitter-sentiment-corpus) - ~ 2000 general tweets
* [Špats](https://github.com/gatis/om) - ~ 6000 general tweets (lowercased)
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}
}
``` |