metadata

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

{   
    "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 - ~ 7000 tweets from politicians and companies
Peisenieks - ~ 1000 general tweets with sentiment annotated by multiple annotators
Vīksna - ~ 4000 general tweets
Nicmanis - ~ 2000 general tweets
Špats - ~ 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." In Proceedings of the 9th Conference Human Language Technologies - The Baltic Perspective (Baltic HLT 2020) (2020).

@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}
}